Overview

Dataset statistics

Number of variables78
Number of observations15435
Missing cells43461
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 MiB
Average record size in memory624.0 B

Variable types

Categorical57
Numeric21

Alerts

periodo has constant value "20201" Constant
estu_estudiante has constant value "ESTUDIANTE" Constant
estu_fechanacimiento has a high cardinality: 3636 distinct values High cardinality
estu_consecutivo has a high cardinality: 15435 distinct values High cardinality
estu_mcpio_reside has a high cardinality: 192 distinct values High cardinality
cole_nombre_establecimiento has a high cardinality: 592 distinct values High cardinality
cole_nombre_sede has a high cardinality: 599 distinct values High cardinality
cole_mcpio_ubicacion has a high cardinality: 103 distinct values High cardinality
estu_mcpio_presentacion has a high cardinality: 64 distinct values High cardinality
estu_cod_reside_depto is highly correlated with estu_cod_reside_mcpio and 6 other fieldsHigh correlation
estu_cod_reside_mcpio is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_dane_establecimiento is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_dane_sede is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_mcpio_ubicacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_depto_ubicacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
estu_cod_mcpio_presentacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
estu_cod_depto_presentacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
punt_lectura_critica is highly correlated with percentil_lectura_critica and 14 other fieldsHigh correlation
percentil_lectura_critica is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_lectura_critica is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_matematicas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_matematicas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_matematicas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_c_naturales is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_c_naturales is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_c_naturales is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_sociales_ciudadanas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_sociales_ciudadanas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_sociales_ciudadanas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_ingles is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_ingles is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_global is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_global is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
estu_cod_reside_depto is highly correlated with estu_cod_reside_mcpio and 6 other fieldsHigh correlation
estu_cod_reside_mcpio is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_dane_establecimiento is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_dane_sede is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_mcpio_ubicacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_depto_ubicacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
estu_cod_mcpio_presentacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
estu_cod_depto_presentacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
punt_lectura_critica is highly correlated with percentil_lectura_critica and 14 other fieldsHigh correlation
percentil_lectura_critica is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_lectura_critica is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_matematicas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_matematicas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_matematicas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_c_naturales is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_c_naturales is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_c_naturales is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_sociales_ciudadanas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_sociales_ciudadanas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_sociales_ciudadanas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_ingles is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_ingles is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_global is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_global is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
estu_cod_reside_depto is highly correlated with estu_cod_reside_mcpio and 6 other fieldsHigh correlation
estu_cod_reside_mcpio is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_dane_establecimiento is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_dane_sede is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_mcpio_ubicacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
cole_cod_depto_ubicacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
estu_cod_mcpio_presentacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
estu_cod_depto_presentacion is highly correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
punt_lectura_critica is highly correlated with percentil_lectura_critica and 14 other fieldsHigh correlation
percentil_lectura_critica is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_lectura_critica is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_matematicas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_matematicas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_matematicas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_c_naturales is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_c_naturales is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_c_naturales is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_sociales_ciudadanas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_sociales_ciudadanas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
desemp_sociales_ciudadanas is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_ingles is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_ingles is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
punt_global is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
percentil_global is highly correlated with punt_lectura_critica and 14 other fieldsHigh correlation
estu_horassemanatrabaja is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
cole_caracter is highly correlated with periodo and 1 other fieldsHigh correlation
estu_dedicacioninternet is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
estu_tipodocumento is highly correlated with periodo and 1 other fieldsHigh correlation
estu_pais_reside is highly correlated with periodo and 2 other fieldsHigh correlation
fami_numlibros is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
desemp_lectura_critica is highly correlated with periodo and 1 other fieldsHigh correlation
fami_tieneconsolavideojuegos is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
cole_depto_ubicacion is highly correlated with periodo and 5 other fieldsHigh correlation
estu_genero is highly correlated with periodo and 1 other fieldsHigh correlation
fami_tienemotocicleta is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
fami_trabajolabormadre is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
cole_sede_principal is highly correlated with estu_horassemanatrabaja and 25 other fieldsHigh correlation
fami_estratovivienda is highly correlated with cole_sede_principal and 4 other fieldsHigh correlation
fami_educacionpadre is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
cole_bilingue is highly correlated with periodo and 4 other fieldsHigh correlation
periodo is highly correlated with estu_horassemanatrabaja and 49 other fieldsHigh correlation
desemp_c_naturales is highly correlated with periodo and 3 other fieldsHigh correlation
desemp_sociales_ciudadanas is highly correlated with periodo and 2 other fieldsHigh correlation
estu_depto_reside is highly correlated with cole_depto_ubicacion and 5 other fieldsHigh correlation
fami_tienecomputador is highly correlated with cole_sede_principal and 4 other fieldsHigh correlation
fami_personashogar is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
fami_tienelavadora is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
cole_area_ubicacion is highly correlated with cole_depto_ubicacion and 2 other fieldsHigh correlation
cole_jornada is highly correlated with periodo and 2 other fieldsHigh correlation
estu_estadoinvestigacion is highly correlated with periodo and 1 other fieldsHigh correlation
fami_comecarnepescadohuevo is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
cole_calendario is highly correlated with periodo and 3 other fieldsHigh correlation
estu_mcpio_presentacion is highly correlated with cole_depto_ubicacion and 8 other fieldsHigh correlation
estu_tiporemuneracion is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
estu_dedicacionlecturadiaria is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
fami_tieneinternet is highly correlated with cole_sede_principal and 4 other fieldsHigh correlation
fami_tieneautomovil is highly correlated with cole_sede_principal and 4 other fieldsHigh correlation
cole_naturaleza is highly correlated with periodo and 2 other fieldsHigh correlation
estu_depto_presentacion is highly correlated with cole_depto_ubicacion and 4 other fieldsHigh correlation
fami_trabajolaborpadre is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
fami_comecerealfrutoslegumbre is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
desemp_matematicas is highly correlated with periodo and 2 other fieldsHigh correlation
estu_tieneetnia is highly correlated with periodo and 2 other fieldsHigh correlation
desemp_ingles is highly correlated with cole_bilingue and 2 other fieldsHigh correlation
estu_privado_libertad is highly correlated with estu_horassemanatrabaja and 25 other fieldsHigh correlation
estu_estudiante is highly correlated with estu_horassemanatrabaja and 49 other fieldsHigh correlation
fami_educacionmadre is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
fami_tienehornomicroogas is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
estu_nacionalidad is highly correlated with estu_pais_reside and 2 other fieldsHigh correlation
fami_comelechederivados is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
fami_cuartoshogar is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
fami_situacioneconomica is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
cole_genero is highly correlated with periodo and 1 other fieldsHigh correlation
estu_generacion_e is highly correlated with periodo and 1 other fieldsHigh correlation
fami_tieneserviciotv is highly correlated with cole_sede_principal and 3 other fieldsHigh correlation
estu_tipodocumento is highly correlated with estu_nacionalidad and 1 other fieldsHigh correlation
estu_nacionalidad is highly correlated with estu_tipodocumento and 1 other fieldsHigh correlation
estu_pais_reside is highly correlated with estu_tipodocumento and 1 other fieldsHigh correlation
estu_tieneetnia is highly correlated with estu_mcpio_presentacionHigh correlation
estu_depto_reside is highly correlated with estu_cod_reside_depto and 14 other fieldsHigh correlation
estu_cod_reside_depto is highly correlated with estu_depto_reside and 11 other fieldsHigh correlation
estu_cod_reside_mcpio is highly correlated with estu_depto_reside and 11 other fieldsHigh correlation
fami_estratovivienda is highly correlated with fami_tieneautomovil and 1 other fieldsHigh correlation
fami_personashogar is highly correlated with fami_cuartoshogarHigh correlation
fami_cuartoshogar is highly correlated with fami_personashogarHigh correlation
fami_educacionpadre is highly correlated with fami_educacionmadre and 10 other fieldsHigh correlation
fami_educacionmadre is highly correlated with fami_educacionpadre and 12 other fieldsHigh correlation
fami_trabajolaborpadre is highly correlated with fami_educacionpadre and 1 other fieldsHigh correlation
fami_trabajolabormadre is highly correlated with fami_educacionmadre and 1 other fieldsHigh correlation
fami_tieneinternet is highly correlated with fami_tieneserviciotv and 3 other fieldsHigh correlation
fami_tieneserviciotv is highly correlated with fami_tieneinternetHigh correlation
fami_tienecomputador is highly correlated with fami_educacionmadre and 6 other fieldsHigh correlation
fami_tienelavadora is highly correlated with fami_tieneinternet and 2 other fieldsHigh correlation
fami_tienehornomicroogas is highly correlated with fami_educacionmadre and 6 other fieldsHigh correlation
fami_tieneautomovil is highly correlated with fami_estratovivienda and 9 other fieldsHigh correlation
fami_tieneconsolavideojuegos is highly correlated with fami_tienehornomicroogas and 1 other fieldsHigh correlation
fami_numlibros is highly correlated with fami_educacionpadre and 7 other fieldsHigh correlation
fami_comelechederivados is highly correlated with fami_comecarnepescadohuevo and 1 other fieldsHigh correlation
fami_comecarnepescadohuevo is highly correlated with fami_comelechederivadosHigh correlation
fami_comecerealfrutoslegumbre is highly correlated with fami_comelechederivadosHigh correlation
cole_codigo_icfes is highly correlated with cole_calendario and 4 other fieldsHigh correlation
cole_cod_dane_establecimiento is highly correlated with estu_depto_reside and 12 other fieldsHigh correlation
cole_naturaleza is highly correlated with cole_cod_dane_establecimiento and 2 other fieldsHigh correlation
cole_calendario is highly correlated with estu_depto_reside and 23 other fieldsHigh correlation
cole_bilingue is highly correlated with estu_depto_reside and 7 other fieldsHigh correlation
cole_caracter is highly correlated with estu_mcpio_presentacion and 1 other fieldsHigh correlation
cole_cod_dane_sede is highly correlated with estu_depto_reside and 12 other fieldsHigh correlation
cole_sede_principal is highly correlated with cole_codigo_icfes and 1 other fieldsHigh correlation
cole_area_ubicacion is highly correlated with cole_depto_ubicacion and 1 other fieldsHigh correlation
cole_jornada is highly correlated with estu_depto_reside and 13 other fieldsHigh correlation
cole_cod_mcpio_ubicacion is highly correlated with estu_depto_reside and 11 other fieldsHigh correlation
cole_cod_depto_ubicacion is highly correlated with estu_depto_reside and 11 other fieldsHigh correlation
cole_depto_ubicacion is highly correlated with estu_depto_reside and 16 other fieldsHigh correlation
estu_privado_libertad is highly correlated with cole_sede_principalHigh correlation
estu_cod_mcpio_presentacion is highly correlated with estu_depto_reside and 11 other fieldsHigh correlation
estu_mcpio_presentacion is highly correlated with estu_tieneetnia and 23 other fieldsHigh correlation
estu_depto_presentacion is highly correlated with estu_depto_reside and 14 other fieldsHigh correlation
estu_cod_depto_presentacion is highly correlated with estu_depto_reside and 11 other fieldsHigh correlation
punt_lectura_critica is highly correlated with cole_calendario and 16 other fieldsHigh correlation
percentil_lectura_critica is highly correlated with punt_lectura_critica and 15 other fieldsHigh correlation
desemp_lectura_critica is highly correlated with fami_educacionpadre and 18 other fieldsHigh correlation
punt_matematicas is highly correlated with punt_lectura_critica and 15 other fieldsHigh correlation
percentil_matematicas is highly correlated with cole_calendario and 16 other fieldsHigh correlation
desemp_matematicas is highly correlated with fami_educacionpadre and 18 other fieldsHigh correlation
punt_c_naturales is highly correlated with cole_calendario and 16 other fieldsHigh correlation
percentil_c_naturales is highly correlated with cole_calendario and 16 other fieldsHigh correlation
desemp_c_naturales is highly correlated with fami_educacionpadre and 18 other fieldsHigh correlation
punt_sociales_ciudadanas is highly correlated with punt_lectura_critica and 15 other fieldsHigh correlation
percentil_sociales_ciudadanas is highly correlated with punt_lectura_critica and 15 other fieldsHigh correlation
desemp_sociales_ciudadanas is highly correlated with fami_educacionpadre and 18 other fieldsHigh correlation
punt_ingles is highly correlated with estu_depto_reside and 20 other fieldsHigh correlation
percentil_ingles is highly correlated with fami_tienecomputador and 22 other fieldsHigh correlation
desemp_ingles is highly correlated with fami_educacionpadre and 19 other fieldsHigh correlation
punt_global is highly correlated with fami_numlibros and 17 other fieldsHigh correlation
percentil_global is highly correlated with fami_tieneautomovil and 18 other fieldsHigh correlation
estu_tieneetnia has 764 (4.9%) missing values Missing
estu_depto_reside has 764 (4.9%) missing values Missing
estu_cod_reside_depto has 764 (4.9%) missing values Missing
estu_mcpio_reside has 764 (4.9%) missing values Missing
estu_cod_reside_mcpio has 764 (4.9%) missing values Missing
fami_estratovivienda has 1558 (10.1%) missing values Missing
fami_personashogar has 1407 (9.1%) missing values Missing
fami_cuartoshogar has 1439 (9.3%) missing values Missing
fami_educacionpadre has 1549 (10.0%) missing values Missing
fami_educacionmadre has 1544 (10.0%) missing values Missing
fami_trabajolaborpadre has 1456 (9.4%) missing values Missing
fami_trabajolabormadre has 1460 (9.5%) missing values Missing
fami_tieneinternet has 1529 (9.9%) missing values Missing
fami_tieneserviciotv has 1544 (10.0%) missing values Missing
fami_tienecomputador has 1422 (9.2%) missing values Missing
fami_tienelavadora has 1427 (9.2%) missing values Missing
fami_tienehornomicroogas has 1437 (9.3%) missing values Missing
fami_tieneautomovil has 1455 (9.4%) missing values Missing
fami_tienemotocicleta has 1451 (9.4%) missing values Missing
fami_tieneconsolavideojuegos has 1451 (9.4%) missing values Missing
fami_numlibros has 1763 (11.4%) missing values Missing
fami_comelechederivados has 1637 (10.6%) missing values Missing
fami_comecarnepescadohuevo has 1535 (9.9%) missing values Missing
fami_comecerealfrutoslegumbre has 1548 (10.0%) missing values Missing
fami_situacioneconomica has 1450 (9.4%) missing values Missing
estu_dedicacionlecturadiaria has 1532 (9.9%) missing values Missing
estu_dedicacioninternet has 1553 (10.1%) missing values Missing
estu_horassemanatrabaja has 1420 (9.2%) missing values Missing
estu_tiporemuneracion has 1458 (9.4%) missing values Missing
cole_bilingue has 2615 (16.9%) missing values Missing
cole_caracter has 928 (6.0%) missing values Missing
estu_consecutivo is uniformly distributed Uniform
percentil_lectura_critica is uniformly distributed Uniform
percentil_matematicas is uniformly distributed Uniform
percentil_c_naturales is uniformly distributed Uniform
percentil_sociales_ciudadanas is uniformly distributed Uniform
percentil_global is uniformly distributed Uniform
estu_consecutivo has unique values Unique

Reproduction

Analysis started2021-12-11 17:31:49.103779
Analysis finished2021-12-11 17:33:09.004758
Duration1 minute and 19.9 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

estu_tipodocumento
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
TI
12506 
CC
2728 
CE
 
81
CR
 
62
PEP
 
28
Other values (5)
 
30

Length

Max length3
Median length2
Mean length2.002656301
Min length2

Characters and Unicode

Total characters30911
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCC
2nd rowCC
3rd rowCC
4th rowCC
5th rowCC

Common Values

ValueCountFrequency (%)
TI12506
81.0%
CC2728
 
17.7%
CE81
 
0.5%
CR62
 
0.4%
PEP28
 
0.2%
RC13
 
0.1%
NES12
 
0.1%
PE3
 
< 0.1%
NIP1
 
< 0.1%
PC1
 
< 0.1%

Length

2021-12-11T12:33:09.159975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:09.225125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ti12506
81.0%
cc2728
 
17.7%
ce81
 
0.5%
cr62
 
0.4%
pep28
 
0.2%
rc13
 
0.1%
nes12
 
0.1%
pe3
 
< 0.1%
nip1
 
< 0.1%
pc1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I12507
40.5%
T12506
40.5%
C5613
18.2%
E124
 
0.4%
R75
 
0.2%
P61
 
0.2%
N13
 
< 0.1%
S12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter30911
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I12507
40.5%
T12506
40.5%
C5613
18.2%
E124
 
0.4%
R75
 
0.2%
P61
 
0.2%
N13
 
< 0.1%
S12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin30911
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I12507
40.5%
T12506
40.5%
C5613
18.2%
E124
 
0.4%
R75
 
0.2%
P61
 
0.2%
N13
 
< 0.1%
S12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I12507
40.5%
T12506
40.5%
C5613
18.2%
E124
 
0.4%
R75
 
0.2%
P61
 
0.2%
N13
 
< 0.1%
S12
 
< 0.1%

estu_nacionalidad
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
COLOMBIA
15321 
VENEZUELA
 
48
ESTADOS UNIDOS
 
16
ESPAÑA
 
12
ECUADOR
 
8
Other values (16)
 
30

Length

Max length22
Median length8
Mean length8.008228053
Min length4

Characters and Unicode

Total characters123607
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA

Common Values

ValueCountFrequency (%)
COLOMBIA15321
99.3%
VENEZUELA48
 
0.3%
ESTADOS UNIDOS16
 
0.1%
ESPAÑA12
 
0.1%
ECUADOR8
 
0.1%
ARGENTINA6
 
< 0.1%
CHILE3
 
< 0.1%
MÉXICO3
 
< 0.1%
ALEMANIA2
 
< 0.1%
BRASIL2
 
< 0.1%
Other values (11)14
 
0.1%

Length

2021-12-11T12:33:09.688107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
colombia15321
99.1%
venezuela48
 
0.3%
estados16
 
0.1%
unidos16
 
0.1%
españa12
 
0.1%
ecuador8
 
0.1%
argentina6
 
< 0.1%
chile3
 
< 0.1%
méxico3
 
< 0.1%
2
 
< 0.1%
Other values (18)25
 
0.2%

Most occurring characters

ValueCountFrequency (%)
O30695
24.8%
A15465
12.5%
L15385
12.4%
I15361
12.4%
C15339
12.4%
M15327
12.4%
B15326
12.4%
E198
 
0.2%
N85
 
0.1%
U78
 
0.1%
Other values (17)348
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter123580
> 99.9%
Space Separator25
 
< 0.1%
Dash Punctuation2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O30695
24.8%
A15465
12.5%
L15385
12.4%
I15361
12.4%
C15339
12.4%
M15327
12.4%
B15326
12.4%
E198
 
0.2%
N85
 
0.1%
U78
 
0.1%
Other values (15)321
 
0.3%
Space Separator
ValueCountFrequency (%)
25
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin123580
> 99.9%
Common27
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O30695
24.8%
A15465
12.5%
L15385
12.4%
I15361
12.4%
C15339
12.4%
M15327
12.4%
B15326
12.4%
E198
 
0.2%
N85
 
0.1%
U78
 
0.1%
Other values (15)321
 
0.3%
Common
ValueCountFrequency (%)
25
92.6%
-2
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII123590
> 99.9%
None17
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O30695
24.8%
A15465
12.5%
L15385
12.4%
I15361
12.4%
C15339
12.4%
M15327
12.4%
B15326
12.4%
E198
 
0.2%
N85
 
0.1%
U78
 
0.1%
Other values (14)331
 
0.3%
None
ValueCountFrequency (%)
Ñ12
70.6%
É3
 
17.6%
Í2
 
11.8%

estu_genero
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size120.7 KiB
M
7846 
F
7588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15434
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M7846
50.8%
F7588
49.2%
(Missing)1
 
< 0.1%

Length

2021-12-11T12:33:09.795493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:09.860809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
m7846
50.8%
f7588
49.2%

Most occurring characters

ValueCountFrequency (%)
M7846
50.8%
F7588
49.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter15434
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M7846
50.8%
F7588
49.2%

Most occurring scripts

ValueCountFrequency (%)
Latin15434
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M7846
50.8%
F7588
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII15434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M7846
50.8%
F7588
49.2%

estu_fechanacimiento
Categorical

HIGH CARDINALITY

Distinct3636
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
2002-06-21T00:00:00.000
 
30
2002-02-18T00:00:00.000
 
26
2003-09-05T00:00:00.000
 
24
2002-06-07T00:00:00.000
 
23
2002-04-25T00:00:00.000
 
22
Other values (3631)
15310 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters355005
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1392 ?
Unique (%)9.0%

Sample

1st row1985-01-01T00:00:00.000
2nd row1995-01-01T00:00:00.000
3rd row1997-01-01T00:00:00.000
4th row2001-01-01T00:00:00.000
5th row2001-02-01T00:00:00.000

Common Values

ValueCountFrequency (%)
2002-06-21T00:00:00.00030
 
0.2%
2002-02-18T00:00:00.00026
 
0.2%
2003-09-05T00:00:00.00024
 
0.2%
2002-06-07T00:00:00.00023
 
0.1%
2002-04-25T00:00:00.00022
 
0.1%
2001-12-07T00:00:00.00022
 
0.1%
2002-03-08T00:00:00.00022
 
0.1%
2002-12-02T00:00:00.00021
 
0.1%
2002-06-29T00:00:00.00021
 
0.1%
2002-06-19T00:00:00.00020
 
0.1%
Other values (3626)15204
98.5%

Length

2021-12-11T12:33:09.929964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2002-06-21t00:00:00.00030
 
0.2%
2002-02-18t00:00:00.00026
 
0.2%
2003-09-05t00:00:00.00024
 
0.2%
2002-06-07t00:00:00.00023
 
0.1%
2002-04-25t00:00:00.00022
 
0.1%
2001-12-07t00:00:00.00022
 
0.1%
2002-03-08t00:00:00.00022
 
0.1%
2002-12-02t00:00:00.00021
 
0.1%
2002-06-29t00:00:00.00021
 
0.1%
2003-02-06t00:00:00.00020
 
0.1%
Other values (3626)15204
98.5%

Most occurring characters

ValueCountFrequency (%)
0191141
53.8%
-30870
 
8.7%
:30870
 
8.7%
225444
 
7.2%
116749
 
4.7%
T15435
 
4.3%
.15435
 
4.3%
38673
 
2.4%
94556
 
1.3%
43716
 
1.0%
Other values (4)12116
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number262395
73.9%
Other Punctuation46305
 
13.0%
Dash Punctuation30870
 
8.7%
Uppercase Letter15435
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0191141
72.8%
225444
 
9.7%
116749
 
6.4%
38673
 
3.3%
94556
 
1.7%
43716
 
1.4%
83197
 
1.2%
52991
 
1.1%
62981
 
1.1%
72947
 
1.1%
Other Punctuation
ValueCountFrequency (%)
:30870
66.7%
.15435
33.3%
Dash Punctuation
ValueCountFrequency (%)
-30870
100.0%
Uppercase Letter
ValueCountFrequency (%)
T15435
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common339570
95.7%
Latin15435
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0191141
56.3%
-30870
 
9.1%
:30870
 
9.1%
225444
 
7.5%
116749
 
4.9%
.15435
 
4.5%
38673
 
2.6%
94556
 
1.3%
43716
 
1.1%
83197
 
0.9%
Other values (3)8919
 
2.6%
Latin
ValueCountFrequency (%)
T15435
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII355005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0191141
53.8%
-30870
 
8.7%
:30870
 
8.7%
225444
 
7.2%
116749
 
4.7%
T15435
 
4.3%
.15435
 
4.3%
38673
 
2.4%
94556
 
1.3%
43716
 
1.0%
Other values (4)12116
 
3.4%

periodo
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
20201
15435 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters77175
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201
2nd row20201
3rd row20201
4th row20201
5th row20201

Common Values

ValueCountFrequency (%)
2020115435
100.0%

Length

2021-12-11T12:33:10.031543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:10.100590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2020115435
100.0%

Most occurring characters

ValueCountFrequency (%)
230870
40.0%
030870
40.0%
115435
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number77175
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
230870
40.0%
030870
40.0%
115435
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common77175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
230870
40.0%
030870
40.0%
115435
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII77175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
230870
40.0%
030870
40.0%
115435
20.0%

estu_consecutivo
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct15435
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
SB11202010045555
 
1
SB11202010001733
 
1
SB11202010013615
 
1
SB11202010042146
 
1
SB11202010048248
 
1
Other values (15430)
15430 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters246960
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15435 ?
Unique (%)100.0%

Sample

1st rowSB11202010045555
2nd rowSB11202010045719
3rd rowSB11202010070662
4th rowSB11202010069926
5th rowSB11202010023181

Common Values

ValueCountFrequency (%)
SB112020100455551
 
< 0.1%
SB112020100017331
 
< 0.1%
SB112020100136151
 
< 0.1%
SB112020100421461
 
< 0.1%
SB112020100482481
 
< 0.1%
SB112020100215101
 
< 0.1%
SB112020100069221
 
< 0.1%
SB112020100076961
 
< 0.1%
SB112020100077561
 
< 0.1%
SB112020100089681
 
< 0.1%
Other values (15425)15425
99.9%

Length

2021-12-11T12:33:10.166549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sb112020100455551
 
< 0.1%
sb112020100710891
 
< 0.1%
sb112020100712001
 
< 0.1%
sb112020100717581
 
< 0.1%
sb112020100706621
 
< 0.1%
sb112020100699261
 
< 0.1%
sb112020100231811
 
< 0.1%
sb112020100579921
 
< 0.1%
sb112020100747181
 
< 0.1%
sb112020100705131
 
< 0.1%
Other values (15425)15425
99.9%

Most occurring characters

ValueCountFrequency (%)
071437
28.9%
155637
22.5%
240200
16.3%
S15435
 
6.2%
B15435
 
6.2%
78559
 
3.5%
37472
 
3.0%
47396
 
3.0%
66582
 
2.7%
56512
 
2.6%
Other values (2)12295
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number216090
87.5%
Uppercase Letter30870
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
071437
33.1%
155637
25.7%
240200
18.6%
78559
 
4.0%
37472
 
3.5%
47396
 
3.4%
66582
 
3.0%
56512
 
3.0%
86175
 
2.9%
96120
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
S15435
50.0%
B15435
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common216090
87.5%
Latin30870
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
071437
33.1%
155637
25.7%
240200
18.6%
78559
 
4.0%
37472
 
3.5%
47396
 
3.4%
66582
 
3.0%
56512
 
3.0%
86175
 
2.9%
96120
 
2.8%
Latin
ValueCountFrequency (%)
S15435
50.0%
B15435
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII246960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
071437
28.9%
155637
22.5%
240200
16.3%
S15435
 
6.2%
B15435
 
6.2%
78559
 
3.5%
37472
 
3.0%
47396
 
3.0%
66582
 
2.7%
56512
 
2.6%
Other values (2)12295
 
5.0%

estu_estudiante
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
ESTUDIANTE
15435 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters154350
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTUDIANTE
2nd rowESTUDIANTE
3rd rowESTUDIANTE
4th rowESTUDIANTE
5th rowESTUDIANTE

Common Values

ValueCountFrequency (%)
ESTUDIANTE15435
100.0%

Length

2021-12-11T12:33:10.271308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:10.337701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
estudiante15435
100.0%

Most occurring characters

ValueCountFrequency (%)
E30870
20.0%
T30870
20.0%
S15435
10.0%
U15435
10.0%
D15435
10.0%
I15435
10.0%
A15435
10.0%
N15435
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter154350
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E30870
20.0%
T30870
20.0%
S15435
10.0%
U15435
10.0%
D15435
10.0%
I15435
10.0%
A15435
10.0%
N15435
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin154350
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E30870
20.0%
T30870
20.0%
S15435
10.0%
U15435
10.0%
D15435
10.0%
I15435
10.0%
A15435
10.0%
N15435
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII154350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E30870
20.0%
T30870
20.0%
S15435
10.0%
U15435
10.0%
D15435
10.0%
I15435
10.0%
A15435
10.0%
N15435
10.0%

estu_pais_reside
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
COLOMBIA
15321 
VENEZUELA
 
48
ESTADOS UNIDOS
 
16
ESPAÑA
 
12
ECUADOR
 
8
Other values (16)
 
30

Length

Max length22
Median length8
Mean length8.008228053
Min length4

Characters and Unicode

Total characters123607
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA

Common Values

ValueCountFrequency (%)
COLOMBIA15321
99.3%
VENEZUELA48
 
0.3%
ESTADOS UNIDOS16
 
0.1%
ESPAÑA12
 
0.1%
ECUADOR8
 
0.1%
ARGENTINA6
 
< 0.1%
CHILE3
 
< 0.1%
MÉXICO3
 
< 0.1%
ALEMANIA2
 
< 0.1%
BRASIL2
 
< 0.1%
Other values (11)14
 
0.1%

Length

2021-12-11T12:33:10.417256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
colombia15321
99.1%
venezuela48
 
0.3%
estados16
 
0.1%
unidos16
 
0.1%
españa12
 
0.1%
ecuador8
 
0.1%
argentina6
 
< 0.1%
chile3
 
< 0.1%
méxico3
 
< 0.1%
2
 
< 0.1%
Other values (18)25
 
0.2%

Most occurring characters

ValueCountFrequency (%)
O30695
24.8%
A15465
12.5%
L15385
12.4%
I15361
12.4%
C15339
12.4%
M15327
12.4%
B15326
12.4%
E198
 
0.2%
N85
 
0.1%
U78
 
0.1%
Other values (17)348
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter123580
> 99.9%
Space Separator25
 
< 0.1%
Dash Punctuation2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O30695
24.8%
A15465
12.5%
L15385
12.4%
I15361
12.4%
C15339
12.4%
M15327
12.4%
B15326
12.4%
E198
 
0.2%
N85
 
0.1%
U78
 
0.1%
Other values (15)321
 
0.3%
Space Separator
ValueCountFrequency (%)
25
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin123580
> 99.9%
Common27
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O30695
24.8%
A15465
12.5%
L15385
12.4%
I15361
12.4%
C15339
12.4%
M15327
12.4%
B15326
12.4%
E198
 
0.2%
N85
 
0.1%
U78
 
0.1%
Other values (15)321
 
0.3%
Common
ValueCountFrequency (%)
25
92.6%
-2
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII123590
> 99.9%
None17
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O30695
24.8%
A15465
12.5%
L15385
12.4%
I15361
12.4%
C15339
12.4%
M15327
12.4%
B15326
12.4%
E198
 
0.2%
N85
 
0.1%
U78
 
0.1%
Other values (14)331
 
0.3%
None
ValueCountFrequency (%)
Ñ12
70.6%
É3
 
17.6%
Í2
 
11.8%

estu_tieneetnia
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing764
Missing (%)4.9%
Memory size120.7 KiB
No
14518 
Si
 
153

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters29342
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No14518
94.1%
Si153
 
1.0%
(Missing)764
 
4.9%

Length

2021-12-11T12:33:10.531259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:10.594352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no14518
99.0%
si153
 
1.0%

Most occurring characters

ValueCountFrequency (%)
N14518
49.5%
o14518
49.5%
S153
 
0.5%
i153
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter14671
50.0%
Lowercase Letter14671
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N14518
99.0%
S153
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
o14518
99.0%
i153
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29342
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N14518
49.5%
o14518
49.5%
S153
 
0.5%
i153
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII29342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N14518
49.5%
o14518
49.5%
S153
 
0.5%
i153
 
0.5%

estu_depto_reside
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)0.2%
Missing764
Missing (%)4.9%
Memory size120.7 KiB
VALLE
6301 
BOGOTÁ
3515 
CUNDINAMARCA
770 
CAUCA
722 
ANTIOQUIA
720 
Other values (22)
2643 

Length

Max length15
Median length5
Mean length6.257787472
Min length4

Characters and Unicode

Total characters91808
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCESAR
2nd rowNARIÑO
3rd rowCAUCA
4th rowPUTUMAYO
5th rowRISARALDA

Common Values

ValueCountFrequency (%)
VALLE6301
40.8%
BOGOTÁ3515
22.8%
CUNDINAMARCA770
 
5.0%
CAUCA722
 
4.7%
ANTIOQUIA720
 
4.7%
ATLANTICO563
 
3.6%
NARIÑO472
 
3.1%
BOLIVAR198
 
1.3%
RISARALDA191
 
1.2%
SANTANDER169
 
1.1%
Other values (17)1050
 
6.8%
(Missing)764
 
4.9%

Length

2021-12-11T12:33:10.669211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valle6301
42.5%
bogotá3515
23.7%
cundinamarca770
 
5.2%
cauca722
 
4.9%
antioquia720
 
4.9%
atlantico563
 
3.8%
nariño472
 
3.2%
santander269
 
1.8%
bolivar198
 
1.3%
risaralda191
 
1.3%
Other values (18)1093
 
7.4%

Most occurring characters

ValueCountFrequency (%)
A15889
17.3%
L13940
15.2%
O9375
10.2%
E7086
7.7%
V6499
 
7.1%
T5961
 
6.5%
N4077
 
4.4%
C4055
 
4.4%
I3870
 
4.2%
B3840
 
4.2%
Other values (14)17216
18.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter91665
99.8%
Space Separator143
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A15889
17.3%
L13940
15.2%
O9375
10.2%
E7086
7.7%
V6499
 
7.1%
T5961
 
6.5%
N4077
 
4.4%
C4055
 
4.4%
I3870
 
4.2%
B3840
 
4.2%
Other values (13)17073
18.6%
Space Separator
ValueCountFrequency (%)
143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin91665
99.8%
Common143
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A15889
17.3%
L13940
15.2%
O9375
10.2%
E7086
7.7%
V6499
 
7.1%
T5961
 
6.5%
N4077
 
4.4%
C4055
 
4.4%
I3870
 
4.2%
B3840
 
4.2%
Other values (13)17073
18.6%
Common
ValueCountFrequency (%)
143
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII87821
95.7%
None3987
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A15889
18.1%
L13940
15.9%
O9375
10.7%
E7086
8.1%
V6499
7.4%
T5961
 
6.8%
N4077
 
4.6%
C4055
 
4.6%
I3870
 
4.4%
B3840
 
4.4%
Other values (12)13229
15.1%
None
ValueCountFrequency (%)
Á3515
88.2%
Ñ472
 
11.8%

estu_cod_reside_depto
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)0.2%
Missing764
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean44.35157794
Minimum5
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:10.772672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q111
median52
Q376
95-th percentile76
Maximum94
Range89
Interquartile range (IQR)65

Descriptive statistics

Standard deviation30.62048243
Coefficient of variation (CV)0.6904034501
Kurtosis-1.871432172
Mean44.35157794
Median Absolute Deviation (MAD)24
Skewness-0.05384218303
Sum650682
Variance937.6139442
MonotonicityNot monotonic
2021-12-11T12:33:10.870378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
766301
40.8%
113515
22.8%
25770
 
5.0%
19722
 
4.7%
5720
 
4.7%
8563
 
3.6%
52472
 
3.1%
13198
 
1.3%
66191
 
1.2%
68169
 
1.1%
Other values (17)1050
 
6.8%
(Missing)764
 
4.9%
ValueCountFrequency (%)
5720
 
4.7%
8563
 
3.6%
113515
22.8%
13198
 
1.3%
1587
 
0.6%
17158
 
1.0%
1823
 
0.1%
19722
 
4.7%
20166
 
1.1%
2340
 
0.3%
ValueCountFrequency (%)
942
 
< 0.1%
8644
 
0.3%
8520
 
0.1%
8112
 
0.1%
766301
40.8%
7337
 
0.2%
68169
 
1.1%
66191
 
1.2%
6342
 
0.3%
54100
 
0.6%

estu_mcpio_reside
Categorical

HIGH CARDINALITY
MISSING

Distinct192
Distinct (%)1.3%
Missing764
Missing (%)4.9%
Memory size120.7 KiB
CALI
4927 
BOGOTÁ D.C.
3515 
POPAYÁN
603 
PALMIRA
547 
BARRANQUILLA
525 
Other values (187)
4554 

Length

Max length27
Median length7
Mean length7.677663418
Min length4

Characters and Unicode

Total characters112639
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.4%

Sample

1st rowSAN DIEGO
2nd rowIPIALES
3rd rowTOTORÓ
4th rowMOCOA
5th rowPEREIRA

Common Values

ValueCountFrequency (%)
CALI4927
31.9%
BOGOTÁ D.C.3515
22.8%
POPAYÁN603
 
3.9%
PALMIRA547
 
3.5%
BARRANQUILLA525
 
3.4%
MEDELLÍN411
 
2.7%
PASTO408
 
2.6%
GUADALAJARA DE BUGA261
 
1.7%
CHÍA245
 
1.6%
CARTAGENA DE INDIAS197
 
1.3%
Other values (182)3032
19.6%
(Missing)764
 
4.9%

Length

2021-12-11T12:33:10.989092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cali4927
25.1%
d.c3515
17.9%
bogotá3515
17.9%
popayán603
 
3.1%
palmira547
 
2.8%
barranquilla525
 
2.7%
de497
 
2.5%
medellín411
 
2.1%
pasto408
 
2.1%
guadalajara261
 
1.3%
Other values (203)4449
22.6%

Most occurring characters

ValueCountFrequency (%)
A16118
14.3%
C10412
 
9.2%
O9445
 
8.4%
L9001
 
8.0%
I8179
 
7.3%
.7030
 
6.2%
D5750
 
5.1%
T5131
 
4.6%
4987
 
4.4%
B4759
 
4.2%
Other values (24)31827
28.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter100619
89.3%
Other Punctuation7030
 
6.2%
Space Separator4987
 
4.4%
Dash Punctuation3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A16118
16.0%
C10412
10.3%
O9445
9.4%
L9001
 
8.9%
I8179
 
8.1%
D5750
 
5.7%
T5131
 
5.1%
B4759
 
4.7%
G4703
 
4.7%
Á4341
 
4.3%
Other values (21)22780
22.6%
Other Punctuation
ValueCountFrequency (%)
.7030
100.0%
Space Separator
ValueCountFrequency (%)
4987
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin100619
89.3%
Common12020
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A16118
16.0%
C10412
10.3%
O9445
9.4%
L9001
 
8.9%
I8179
 
8.1%
D5750
 
5.7%
T5131
 
5.1%
B4759
 
4.7%
G4703
 
4.7%
Á4341
 
4.3%
Other values (21)22780
22.6%
Common
ValueCountFrequency (%)
.7030
58.5%
4987
41.5%
-3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII107138
95.1%
None5501
 
4.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A16118
15.0%
C10412
 
9.7%
O9445
 
8.8%
L9001
 
8.4%
I8179
 
7.6%
.7030
 
6.6%
D5750
 
5.4%
T5131
 
4.8%
4987
 
4.7%
B4759
 
4.4%
Other values (17)26326
24.6%
None
ValueCountFrequency (%)
Á4341
78.9%
Í978
 
17.8%
Ú80
 
1.5%
Ó57
 
1.0%
É28
 
0.5%
Ñ16
 
0.3%
Ü1
 
< 0.1%

estu_cod_reside_mcpio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct192
Distinct (%)1.3%
Missing764
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean44437.50971
Minimum5001
Maximum94001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:11.110140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile8001
Q111001
median52001
Q376001
95-th percentile76520
Maximum94001
Range89000
Interquartile range (IQR)65000

Descriptive statistics

Standard deviation30630.07403
Coefficient of variation (CV)0.6892842157
Kurtosis-1.870391044
Mean44437.50971
Median Absolute Deviation (MAD)24519
Skewness-0.05512378118
Sum651942705
Variance938201435.2
MonotonicityNot monotonic
2021-12-11T12:33:11.245247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
760014927
31.9%
110013515
22.8%
19001603
 
3.9%
76520547
 
3.5%
8001525
 
3.4%
5001411
 
2.7%
52001408
 
2.6%
76111261
 
1.7%
25175245
 
1.6%
13001197
 
1.3%
Other values (182)3032
19.6%
(Missing)764
 
4.9%
ValueCountFrequency (%)
5001411
2.7%
504513
 
0.1%
50882
 
< 0.1%
51251
 
< 0.1%
51471
 
< 0.1%
51481
 
< 0.1%
51541
 
< 0.1%
51971
 
< 0.1%
5266128
 
0.8%
53181
 
< 0.1%
ValueCountFrequency (%)
940012
 
< 0.1%
863201
 
< 0.1%
8600143
0.3%
851392
 
< 0.1%
850101
 
< 0.1%
8500117
 
0.1%
8100112
 
0.1%
768954
 
< 0.1%
7689292
0.6%
768696
 
< 0.1%

fami_estratovivienda
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing1558
Missing (%)10.1%
Memory size120.7 KiB
Estrato 3
3312 
Estrato 4
2795 
Estrato 5
2377 
Estrato 2
2318 
Estrato 6
2134 
Other values (2)
941 

Length

Max length11
Median length9
Mean length9.021186135
Min length9

Characters and Unicode

Total characters125187
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstrato 1
2nd rowEstrato 1
3rd rowEstrato 1
4th rowEstrato 6
5th rowEstrato 1

Common Values

ValueCountFrequency (%)
Estrato 33312
21.5%
Estrato 42795
18.1%
Estrato 52377
15.4%
Estrato 22318
15.0%
Estrato 62134
13.8%
Estrato 1794
 
5.1%
Sin Estrato147
 
1.0%
(Missing)1558
10.1%

Length

2021-12-11T12:33:11.370727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:11.453914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
estrato13877
50.0%
33312
 
11.9%
42795
 
10.1%
52377
 
8.6%
22318
 
8.4%
62134
 
7.7%
1794
 
2.9%
sin147
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t27754
22.2%
E13877
11.1%
s13877
11.1%
r13877
11.1%
a13877
11.1%
o13877
11.1%
13877
11.1%
33312
 
2.6%
42795
 
2.2%
52377
 
1.9%
Other values (6)5687
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter83556
66.7%
Uppercase Letter14024
 
11.2%
Space Separator13877
 
11.1%
Decimal Number13730
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t27754
33.2%
s13877
16.6%
r13877
16.6%
a13877
16.6%
o13877
16.6%
i147
 
0.2%
n147
 
0.2%
Decimal Number
ValueCountFrequency (%)
33312
24.1%
42795
20.4%
52377
17.3%
22318
16.9%
62134
15.5%
1794
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
E13877
99.0%
S147
 
1.0%
Space Separator
ValueCountFrequency (%)
13877
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin97580
77.9%
Common27607
 
22.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t27754
28.4%
E13877
14.2%
s13877
14.2%
r13877
14.2%
a13877
14.2%
o13877
14.2%
S147
 
0.2%
i147
 
0.2%
n147
 
0.2%
Common
ValueCountFrequency (%)
13877
50.3%
33312
 
12.0%
42795
 
10.1%
52377
 
8.6%
22318
 
8.4%
62134
 
7.7%
1794
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII125187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t27754
22.2%
E13877
11.1%
s13877
11.1%
r13877
11.1%
a13877
11.1%
o13877
11.1%
13877
11.1%
33312
 
2.6%
42795
 
2.2%
52377
 
1.9%
Other values (6)5687
 
4.5%

fami_personashogar
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing1407
Missing (%)9.1%
Memory size120.7 KiB
3 a 4
8763 
5 a 6
3164 
1 a 2
1478 
7 a 8
 
473
9 o más
 
150

Length

Max length7
Median length5
Mean length5.0213858
Min length5

Characters and Unicode

Total characters70440
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5 a 6
2nd row3 a 4
3rd row1 a 2
4th row3 a 4
5th row7 a 8

Common Values

ValueCountFrequency (%)
3 a 48763
56.8%
5 a 63164
 
20.5%
1 a 21478
 
9.6%
7 a 8473
 
3.1%
9 o más150
 
1.0%
(Missing)1407
 
9.1%

Length

2021-12-11T12:33:11.570415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:11.822916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
a13878
33.0%
38763
20.8%
48763
20.8%
53164
 
7.5%
63164
 
7.5%
11478
 
3.5%
21478
 
3.5%
7473
 
1.1%
8473
 
1.1%
9150
 
0.4%
Other values (2)300
 
0.7%

Most occurring characters

ValueCountFrequency (%)
28056
39.8%
a13878
19.7%
38763
 
12.4%
48763
 
12.4%
53164
 
4.5%
63164
 
4.5%
11478
 
2.1%
21478
 
2.1%
7473
 
0.7%
8473
 
0.7%
Other values (5)750
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator28056
39.8%
Decimal Number27906
39.6%
Lowercase Letter14478
20.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
38763
31.4%
48763
31.4%
53164
 
11.3%
63164
 
11.3%
11478
 
5.3%
21478
 
5.3%
7473
 
1.7%
8473
 
1.7%
9150
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
a13878
95.9%
o150
 
1.0%
m150
 
1.0%
á150
 
1.0%
s150
 
1.0%
Space Separator
ValueCountFrequency (%)
28056
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common55962
79.4%
Latin14478
 
20.6%

Most frequent character per script

Common
ValueCountFrequency (%)
28056
50.1%
38763
 
15.7%
48763
 
15.7%
53164
 
5.7%
63164
 
5.7%
11478
 
2.6%
21478
 
2.6%
7473
 
0.8%
8473
 
0.8%
9150
 
0.3%
Latin
ValueCountFrequency (%)
a13878
95.9%
o150
 
1.0%
m150
 
1.0%
á150
 
1.0%
s150
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70290
99.8%
None150
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28056
39.9%
a13878
19.7%
38763
 
12.5%
48763
 
12.5%
53164
 
4.5%
63164
 
4.5%
11478
 
2.1%
21478
 
2.1%
7473
 
0.7%
8473
 
0.7%
Other values (4)600
 
0.9%
None
ValueCountFrequency (%)
á150
100.0%

fami_cuartoshogar
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing1439
Missing (%)9.3%
Memory size120.7 KiB
Tres
6433 
Dos
3430 
Cuatro
2626 
Cinco
737 
Uno
 
441

Length

Max length10
Median length4
Mean length4.292369248
Min length3

Characters and Unicode

Total characters60076
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUno
2nd rowCuatro
3rd rowUno
4th rowCuatro
5th rowCuatro

Common Values

ValueCountFrequency (%)
Tres6433
41.7%
Dos3430
22.2%
Cuatro2626
17.0%
Cinco737
 
4.8%
Uno441
 
2.9%
Seis o mas329
 
2.1%
(Missing)1439
 
9.3%

Length

2021-12-11T12:33:11.908693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:11.980702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
tres6433
43.9%
dos3430
23.4%
cuatro2626
17.9%
cinco737
 
5.0%
uno441
 
3.0%
seis329
 
2.2%
o329
 
2.2%
mas329
 
2.2%

Most occurring characters

ValueCountFrequency (%)
s10521
17.5%
r9059
15.1%
o7563
12.6%
e6762
11.3%
T6433
10.7%
D3430
 
5.7%
C3363
 
5.6%
a2955
 
4.9%
u2626
 
4.4%
t2626
 
4.4%
Other values (7)4738
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter45422
75.6%
Uppercase Letter13996
 
23.3%
Space Separator658
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s10521
23.2%
r9059
19.9%
o7563
16.7%
e6762
14.9%
a2955
 
6.5%
u2626
 
5.8%
t2626
 
5.8%
n1178
 
2.6%
i1066
 
2.3%
c737
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
T6433
46.0%
D3430
24.5%
C3363
24.0%
U441
 
3.2%
S329
 
2.4%
Space Separator
ValueCountFrequency (%)
658
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin59418
98.9%
Common658
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s10521
17.7%
r9059
15.2%
o7563
12.7%
e6762
11.4%
T6433
10.8%
D3430
 
5.8%
C3363
 
5.7%
a2955
 
5.0%
u2626
 
4.4%
t2626
 
4.4%
Other values (6)4080
 
6.9%
Common
ValueCountFrequency (%)
658
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII60076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s10521
17.5%
r9059
15.1%
o7563
12.6%
e6762
11.3%
T6433
10.7%
D3430
 
5.7%
C3363
 
5.6%
a2955
 
4.9%
u2626
 
4.4%
t2626
 
4.4%
Other values (7)4738
7.9%

fami_educacionpadre
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)0.1%
Missing1549
Missing (%)10.0%
Memory size120.7 KiB
Educación profesional completa
4008 
Postgrado
2734 
Secundaria (Bachillerato) completa
2323 
Técnica o tecnológica completa
1011 
No sabe
801 
Other values (7)
3009 

Length

Max length36
Median length30
Mean length24.22641509
Min length7

Characters and Unicode

Total characters336408
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimaria incompleta
2nd rowSecundaria (Bachillerato) incompleta
3rd rowTécnica o tecnológica completa
4th rowEducación profesional completa
5th rowPrimaria completa

Common Values

ValueCountFrequency (%)
Educación profesional completa4008
26.0%
Postgrado2734
17.7%
Secundaria (Bachillerato) completa2323
15.1%
Técnica o tecnológica completa1011
 
6.6%
No sabe801
 
5.2%
Secundaria (Bachillerato) incompleta797
 
5.2%
Primaria incompleta677
 
4.4%
Educación profesional incompleta574
 
3.7%
Primaria completa374
 
2.4%
Técnica o tecnológica incompleta239
 
1.5%
Other values (2)348
 
2.3%
(Missing)1549
 
10.0%

Length

2021-12-11T12:33:12.075937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa7716
22.0%
educación4582
13.1%
profesional4582
13.1%
secundaria3120
8.9%
bachillerato3120
8.9%
postgrado2734
 
7.8%
incompleta2287
 
6.5%
técnica1250
 
3.6%
o1250
 
3.6%
tecnológica1250
 
3.6%
Other values (5)3140
9.0%

Most occurring characters

ValueCountFrequency (%)
a39923
11.9%
o31404
 
9.3%
c30546
 
9.1%
e22876
 
6.8%
i22641
 
6.7%
l22214
 
6.6%
21145
 
6.3%
n17489
 
5.2%
t17107
 
5.1%
r15658
 
4.7%
Other values (20)95405
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter291878
86.8%
Space Separator21145
 
6.3%
Uppercase Letter17145
 
5.1%
Open Punctuation3120
 
0.9%
Close Punctuation3120
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a39923
13.7%
o31404
10.8%
c30546
10.5%
e22876
 
7.8%
i22641
 
7.8%
l22214
 
7.6%
n17489
 
6.0%
t17107
 
5.9%
r15658
 
5.4%
p14724
 
5.0%
Other values (10)57296
19.6%
Uppercase Letter
ValueCountFrequency (%)
E4582
26.7%
P3785
22.1%
S3120
18.2%
B3120
18.2%
T1250
 
7.3%
N1149
 
6.7%
A139
 
0.8%
Space Separator
ValueCountFrequency (%)
21145
100.0%
Open Punctuation
ValueCountFrequency (%)
(3120
100.0%
Close Punctuation
ValueCountFrequency (%)
)3120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin309023
91.9%
Common27385
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a39923
12.9%
o31404
10.2%
c30546
9.9%
e22876
 
7.4%
i22641
 
7.3%
l22214
 
7.2%
n17489
 
5.7%
t17107
 
5.5%
r15658
 
5.1%
p14724
 
4.8%
Other values (17)74441
24.1%
Common
ValueCountFrequency (%)
21145
77.2%
(3120
 
11.4%
)3120
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII329326
97.9%
None7082
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a39923
12.1%
o31404
 
9.5%
c30546
 
9.3%
e22876
 
6.9%
i22641
 
6.9%
l22214
 
6.7%
21145
 
6.4%
n17489
 
5.3%
t17107
 
5.2%
r15658
 
4.8%
Other values (18)88323
26.8%
None
ValueCountFrequency (%)
ó5832
82.3%
é1250
 
17.7%

fami_educacionmadre
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)0.1%
Missing1544
Missing (%)10.0%
Memory size120.7 KiB
Educación profesional completa
4880 
Postgrado
2571 
Secundaria (Bachillerato) completa
2077 
Técnica o tecnológica completa
1326 
Educación profesional incompleta
688 
Other values (7)
2349 

Length

Max length36
Median length30
Mean length25.65661219
Min length7

Characters and Unicode

Total characters356396
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimaria incompleta
2nd rowSecundaria (Bachillerato) incompleta
3rd rowEducación profesional completa
4th rowSecundaria (Bachillerato) completa
5th rowPrimaria incompleta

Common Values

ValueCountFrequency (%)
Educación profesional completa4880
31.6%
Postgrado2571
16.7%
Secundaria (Bachillerato) completa2077
13.5%
Técnica o tecnológica completa1326
 
8.6%
Educación profesional incompleta688
 
4.5%
Secundaria (Bachillerato) incompleta681
 
4.4%
Primaria incompleta522
 
3.4%
Técnica o tecnológica incompleta341
 
2.2%
Primaria completa341
 
2.2%
No sabe327
 
2.1%
Other values (2)137
 
0.9%
(Missing)1544
 
10.0%

Length

2021-12-11T12:33:12.180906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa8624
23.5%
educación5568
15.1%
profesional5568
15.1%
secundaria2758
 
7.5%
bachillerato2758
 
7.5%
postgrado2571
 
7.0%
incompleta2232
 
6.1%
técnica1667
 
4.5%
o1667
 
4.5%
tecnológica1667
 
4.5%
Other values (5)1680
 
4.6%

Most occurring characters

ValueCountFrequency (%)
a41008
11.5%
c34202
 
9.6%
o33690
 
9.5%
i24081
 
6.8%
e23934
 
6.7%
l23633
 
6.6%
22869
 
6.4%
n19682
 
5.5%
t17852
 
5.0%
p16450
 
4.6%
Other values (20)98995
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter311336
87.4%
Space Separator22869
 
6.4%
Uppercase Letter16675
 
4.7%
Open Punctuation2758
 
0.8%
Close Punctuation2758
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a41008
13.2%
c34202
11.0%
o33690
10.8%
i24081
 
7.7%
e23934
 
7.7%
l23633
 
7.6%
n19682
 
6.3%
t17852
 
5.7%
p16450
 
5.3%
r15381
 
4.9%
Other values (10)61423
19.7%
Uppercase Letter
ValueCountFrequency (%)
E5568
33.4%
P3434
20.6%
S2758
16.5%
B2758
16.5%
T1667
 
10.0%
N464
 
2.8%
A26
 
0.2%
Space Separator
ValueCountFrequency (%)
22869
100.0%
Open Punctuation
ValueCountFrequency (%)
(2758
100.0%
Close Punctuation
ValueCountFrequency (%)
)2758
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin328011
92.0%
Common28385
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a41008
12.5%
c34202
10.4%
o33690
10.3%
i24081
 
7.3%
e23934
 
7.3%
l23633
 
7.2%
n19682
 
6.0%
t17852
 
5.4%
p16450
 
5.0%
r15381
 
4.7%
Other values (17)78098
23.8%
Common
ValueCountFrequency (%)
22869
80.6%
(2758
 
9.7%
)2758
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII347494
97.5%
None8902
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a41008
11.8%
c34202
 
9.8%
o33690
 
9.7%
i24081
 
6.9%
e23934
 
6.9%
l23633
 
6.8%
22869
 
6.6%
n19682
 
5.7%
t17852
 
5.1%
p16450
 
4.7%
Other values (18)90093
25.9%
None
ValueCountFrequency (%)
ó7235
81.3%
é1667
 
18.7%

fami_trabajolaborpadre
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)0.1%
Missing1456
Missing (%)9.4%
Memory size120.7 KiB
Trabaja como profesional (por ejemplo médico, abogado, ingeniero)
4335 
Es dueño de un negocio grande, tiene un cargo de nivel directivo o gerencial
1618 
Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etc
1289 
Trabaja por cuenta propia (por ejemplo plomero, electricista)
1037 
No aplica
993 
Other values (8)
4707 

Length

Max length100
Median length65
Mean length57.19514987
Min length7

Characters and Unicode

Total characters799531
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrabaja en el hogar, no trabaja o estudia
2nd rowTrabaja en el hogar, no trabaja o estudia
3rd rowEs dueño de un negocio grande, tiene un cargo de nivel directivo o gerencial
4th rowTrabaja como profesional (por ejemplo médico, abogado, ingeniero)
5th rowTrabaja como personal de limpieza, mantenimiento, seguridad o construcción

Common Values

ValueCountFrequency (%)
Trabaja como profesional (por ejemplo médico, abogado, ingeniero)4335
28.1%
Es dueño de un negocio grande, tiene un cargo de nivel directivo o gerencial1618
 
10.5%
Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etc1289
 
8.4%
Trabaja por cuenta propia (por ejemplo plomero, electricista)1037
 
6.7%
No aplica993
 
6.4%
No sabe944
 
6.1%
Es operario de máquinas o conduce vehículos (taxita, chofer)783
 
5.1%
Es vendedor o trabaja en atención al público667
 
4.3%
Trabaja en el hogar, no trabaja o estudia643
 
4.2%
Tiene un trabajo de tipo auxiliar administrativo (por ejemplo, secretario o asistente)490
 
3.2%
Other values (3)1180
 
7.6%
(Missing)1456
 
9.4%

Length

2021-12-11T12:33:12.280515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
por8188
 
6.8%
trabaja7645
 
6.4%
ejemplo7151
 
6.0%
o6209
 
5.2%
de6118
 
5.1%
un5015
 
4.2%
es4756
 
4.0%
tiene4686
 
3.9%
como4655
 
3.9%
profesional4335
 
3.6%
Other values (52)61400
51.1%

Most occurring characters

ValueCountFrequency (%)
106179
13.3%
o92291
11.5%
e85052
 
10.6%
a64202
 
8.0%
i47824
 
6.0%
r44439
 
5.6%
n43823
 
5.5%
p33202
 
4.2%
c29748
 
3.7%
d27860
 
3.5%
Other values (27)224911
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter646647
80.9%
Space Separator106179
 
13.3%
Other Punctuation18147
 
2.3%
Uppercase Letter13979
 
1.7%
Open Punctuation7934
 
1.0%
Close Punctuation6645
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o92291
14.3%
e85052
13.2%
a64202
9.9%
i47824
 
7.4%
r44439
 
6.9%
n43823
 
6.8%
p33202
 
5.1%
c29748
 
4.6%
d27860
 
4.3%
l25055
 
3.9%
Other values (19)153151
23.7%
Uppercase Letter
ValueCountFrequency (%)
T6825
48.8%
E4756
34.0%
N1937
 
13.9%
P461
 
3.3%
Space Separator
ValueCountFrequency (%)
106179
100.0%
Other Punctuation
ValueCountFrequency (%)
,18147
100.0%
Open Punctuation
ValueCountFrequency (%)
(7934
100.0%
Close Punctuation
ValueCountFrequency (%)
)6645
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin660626
82.6%
Common138905
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o92291
14.0%
e85052
12.9%
a64202
 
9.7%
i47824
 
7.2%
r44439
 
6.7%
n43823
 
6.6%
p33202
 
5.0%
c29748
 
4.5%
d27860
 
4.2%
l25055
 
3.8%
Other values (23)167130
25.3%
Common
ValueCountFrequency (%)
106179
76.4%
,18147
 
13.1%
(7934
 
5.7%
)6645
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII786491
98.4%
None13040
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
106179
13.5%
o92291
11.7%
e85052
10.8%
a64202
 
8.2%
i47824
 
6.1%
r44439
 
5.7%
n43823
 
5.6%
p33202
 
4.2%
c29748
 
3.8%
d27860
 
3.5%
Other values (21)211871
26.9%
None
ValueCountFrequency (%)
é4335
33.2%
ñ4196
32.2%
í2072
15.9%
ó987
 
7.6%
á783
 
6.0%
ú667
 
5.1%

fami_trabajolabormadre
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)0.1%
Missing1460
Missing (%)9.5%
Memory size120.7 KiB
Trabaja como profesional (por ejemplo médico, abogado, ingeniero)
4209 
Trabaja en el hogar, no trabaja o estudia
3351 
Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etc
1317 
Tiene un trabajo de tipo auxiliar administrativo (por ejemplo, secretario o asistente)
1172 
Es dueño de un negocio grande, tiene un cargo de nivel directivo o gerencial
943 
Other values (8)
2983 

Length

Max length100
Median length65
Mean length59.69903399
Min length7

Characters and Unicode

Total characters834294
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrabaja por cuenta propia (por ejemplo plomero, electricista)
2nd rowTrabaja en el hogar, no trabaja o estudia
3rd rowTrabaja como profesional (por ejemplo médico, abogado, ingeniero)
4th rowTrabaja en el hogar, no trabaja o estudia
5th rowTrabaja en el hogar, no trabaja o estudia

Common Values

ValueCountFrequency (%)
Trabaja como profesional (por ejemplo médico, abogado, ingeniero)4209
27.3%
Trabaja en el hogar, no trabaja o estudia3351
21.7%
Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etc1317
 
8.5%
Tiene un trabajo de tipo auxiliar administrativo (por ejemplo, secretario o asistente)1172
 
7.6%
Es dueño de un negocio grande, tiene un cargo de nivel directivo o gerencial943
 
6.1%
Es vendedor o trabaja en atención al público888
 
5.8%
No aplica520
 
3.4%
Trabaja por cuenta propia (por ejemplo plomero, electricista)507
 
3.3%
Trabaja como personal de limpieza, mantenimiento, seguridad o construcción427
 
2.8%
No sabe268
 
1.7%
Other values (3)373
 
2.4%
(Missing)1460
 
9.5%

Length

2021-12-11T12:33:12.381509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
trabaja12733
 
9.9%
o8292
 
6.5%
por7712
 
6.0%
ejemplo7205
 
5.6%
no5456
 
4.3%
de4899
 
3.8%
tiene4749
 
3.7%
como4636
 
3.6%
un4375
 
3.4%
en4239
 
3.3%
Other values (52)63955
49.9%

Most occurring characters

ValueCountFrequency (%)
114276
13.7%
o90973
 
10.9%
e84787
 
10.2%
a82075
 
9.8%
i48607
 
5.8%
r47629
 
5.7%
n45790
 
5.5%
p30860
 
3.7%
d27526
 
3.3%
t26929
 
3.2%
Other values (27)234842
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter673366
80.7%
Space Separator114276
 
13.7%
Other Punctuation19390
 
2.3%
Uppercase Letter13975
 
1.7%
Open Punctuation7302
 
0.9%
Close Punctuation5985
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o90973
13.5%
e84787
12.6%
a82075
12.2%
i48607
 
7.2%
r47629
 
7.1%
n45790
 
6.8%
p30860
 
4.6%
d27526
 
4.1%
t26929
 
4.0%
l24912
 
3.7%
Other values (19)163278
24.2%
Uppercase Letter
ValueCountFrequency (%)
T9666
69.2%
E3342
 
23.9%
N788
 
5.6%
P179
 
1.3%
Space Separator
ValueCountFrequency (%)
114276
100.0%
Other Punctuation
ValueCountFrequency (%)
,19390
100.0%
Open Punctuation
ValueCountFrequency (%)
(7302
100.0%
Close Punctuation
ValueCountFrequency (%)
)5985
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin687341
82.4%
Common146953
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o90973
13.2%
e84787
12.3%
a82075
11.9%
i48607
 
7.1%
r47629
 
6.9%
n45790
 
6.7%
p30860
 
4.5%
d27526
 
4.0%
t26929
 
3.9%
l24912
 
3.6%
Other values (23)177253
25.8%
Common
ValueCountFrequency (%)
114276
77.8%
,19390
 
13.2%
(7302
 
5.0%
)5985
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII822794
98.6%
None11500
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
114276
13.9%
o90973
11.1%
e84787
 
10.3%
a82075
 
10.0%
i48607
 
5.9%
r47629
 
5.8%
n45790
 
5.6%
p30860
 
3.8%
d27526
 
3.3%
t26929
 
3.3%
Other values (21)223342
27.1%
None
ValueCountFrequency (%)
é4209
36.6%
ñ3577
31.1%
í1414
 
12.3%
ó1315
 
11.4%
ú888
 
7.7%
á97
 
0.8%

fami_tieneinternet
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1529
Missing (%)9.9%
Memory size120.7 KiB
Si
13231 
No
 
675

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters27812
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si13231
85.7%
No675
 
4.4%
(Missing)1529
 
9.9%

Length

2021-12-11T12:33:12.479924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:12.547530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
si13231
95.1%
no675
 
4.9%

Most occurring characters

ValueCountFrequency (%)
S13231
47.6%
i13231
47.6%
N675
 
2.4%
o675
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13906
50.0%
Lowercase Letter13906
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S13231
95.1%
N675
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
i13231
95.1%
o675
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin27812
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S13231
47.6%
i13231
47.6%
N675
 
2.4%
o675
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII27812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S13231
47.6%
i13231
47.6%
N675
 
2.4%
o675
 
2.4%

fami_tieneserviciotv
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1544
Missing (%)10.0%
Memory size120.7 KiB
Si
12672 
No
 
1219

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters27782
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowSi
5th rowNo

Common Values

ValueCountFrequency (%)
Si12672
82.1%
No1219
 
7.9%
(Missing)1544
 
10.0%

Length

2021-12-11T12:33:12.617619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:12.679778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
si12672
91.2%
no1219
 
8.8%

Most occurring characters

ValueCountFrequency (%)
S12672
45.6%
i12672
45.6%
N1219
 
4.4%
o1219
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13891
50.0%
Lowercase Letter13891
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S12672
91.2%
N1219
 
8.8%
Lowercase Letter
ValueCountFrequency (%)
i12672
91.2%
o1219
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Latin27782
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S12672
45.6%
i12672
45.6%
N1219
 
4.4%
o1219
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII27782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S12672
45.6%
i12672
45.6%
N1219
 
4.4%
o1219
 
4.4%

fami_tienecomputador
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1422
Missing (%)9.2%
Memory size120.7 KiB
Si
12671 
No
1342 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters28026
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si12671
82.1%
No1342
 
8.7%
(Missing)1422
 
9.2%

Length

2021-12-11T12:33:12.744434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:12.806037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
si12671
90.4%
no1342
 
9.6%

Most occurring characters

ValueCountFrequency (%)
S12671
45.2%
i12671
45.2%
N1342
 
4.8%
o1342
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter14013
50.0%
Lowercase Letter14013
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S12671
90.4%
N1342
 
9.6%
Lowercase Letter
ValueCountFrequency (%)
i12671
90.4%
o1342
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin28026
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S12671
45.2%
i12671
45.2%
N1342
 
4.8%
o1342
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII28026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S12671
45.2%
i12671
45.2%
N1342
 
4.8%
o1342
 
4.8%

fami_tienelavadora
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1427
Missing (%)9.2%
Memory size120.7 KiB
Si
13076 
No
 
932

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters28016
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si13076
84.7%
No932
 
6.0%
(Missing)1427
 
9.2%

Length

2021-12-11T12:33:12.869771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:12.935173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
si13076
93.3%
no932
 
6.7%

Most occurring characters

ValueCountFrequency (%)
S13076
46.7%
i13076
46.7%
N932
 
3.3%
o932
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter14008
50.0%
Lowercase Letter14008
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S13076
93.3%
N932
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
i13076
93.3%
o932
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin28016
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S13076
46.7%
i13076
46.7%
N932
 
3.3%
o932
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII28016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S13076
46.7%
i13076
46.7%
N932
 
3.3%
o932
 
3.3%

fami_tienehornomicroogas
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1437
Missing (%)9.3%
Memory size120.7 KiB
Si
11353 
No
2645 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters27996
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si11353
73.6%
No2645
 
17.1%
(Missing)1437
 
9.3%

Length

2021-12-11T12:33:13.002114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:13.070487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
si11353
81.1%
no2645
 
18.9%

Most occurring characters

ValueCountFrequency (%)
S11353
40.6%
i11353
40.6%
N2645
 
9.4%
o2645
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13998
50.0%
Lowercase Letter13998
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S11353
81.1%
N2645
 
18.9%
Lowercase Letter
ValueCountFrequency (%)
i11353
81.1%
o2645
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
Latin27996
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S11353
40.6%
i11353
40.6%
N2645
 
9.4%
o2645
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII27996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S11353
40.6%
i11353
40.6%
N2645
 
9.4%
o2645
 
9.4%

fami_tieneautomovil
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1455
Missing (%)9.4%
Memory size120.7 KiB
Si
9756 
No
4224 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters27960
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowSi
5th rowNo

Common Values

ValueCountFrequency (%)
Si9756
63.2%
No4224
27.4%
(Missing)1455
 
9.4%

Length

2021-12-11T12:33:13.139696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:13.203044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
si9756
69.8%
no4224
30.2%

Most occurring characters

ValueCountFrequency (%)
S9756
34.9%
i9756
34.9%
N4224
15.1%
o4224
15.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13980
50.0%
Lowercase Letter13980
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S9756
69.8%
N4224
30.2%
Lowercase Letter
ValueCountFrequency (%)
i9756
69.8%
o4224
30.2%

Most occurring scripts

ValueCountFrequency (%)
Latin27960
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S9756
34.9%
i9756
34.9%
N4224
15.1%
o4224
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII27960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S9756
34.9%
i9756
34.9%
N4224
15.1%
o4224
15.1%

fami_tienemotocicleta
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1451
Missing (%)9.4%
Memory size120.7 KiB
No
9962 
Si
4022 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters27968
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No9962
64.5%
Si4022
26.1%
(Missing)1451
 
9.4%

Length

2021-12-11T12:33:13.273776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:13.341153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no9962
71.2%
si4022
28.8%

Most occurring characters

ValueCountFrequency (%)
N9962
35.6%
o9962
35.6%
S4022
14.4%
i4022
14.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13984
50.0%
Lowercase Letter13984
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N9962
71.2%
S4022
28.8%
Lowercase Letter
ValueCountFrequency (%)
o9962
71.2%
i4022
28.8%

Most occurring scripts

ValueCountFrequency (%)
Latin27968
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N9962
35.6%
o9962
35.6%
S4022
14.4%
i4022
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII27968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N9962
35.6%
o9962
35.6%
S4022
14.4%
i4022
14.4%

fami_tieneconsolavideojuegos
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1451
Missing (%)9.4%
Memory size120.7 KiB
Si
7815 
No
6169 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters27968
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Si7815
50.6%
No6169
40.0%
(Missing)1451
 
9.4%

Length

2021-12-11T12:33:13.412825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:13.474047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
si7815
55.9%
no6169
44.1%

Most occurring characters

ValueCountFrequency (%)
S7815
27.9%
i7815
27.9%
N6169
22.1%
o6169
22.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13984
50.0%
Lowercase Letter13984
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S7815
55.9%
N6169
44.1%
Lowercase Letter
ValueCountFrequency (%)
i7815
55.9%
o6169
44.1%

Most occurring scripts

ValueCountFrequency (%)
Latin27968
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S7815
27.9%
i7815
27.9%
N6169
22.1%
o6169
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII27968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S7815
27.9%
i7815
27.9%
N6169
22.1%
o6169
22.1%

fami_numlibros
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1763
Missing (%)11.4%
Memory size120.7 KiB
26 A 100 LIBROS
4842 
MÁS DE 100 LIBROS
3272 
11 A 25 LIBROS
3066 
0 A 10 LIBROS
2492 

Length

Max length17
Median length15
Mean length14.88984786
Min length13

Characters and Unicode

Total characters203574
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0 A 10 LIBROS
2nd row0 A 10 LIBROS
3rd row11 A 25 LIBROS
4th row26 A 100 LIBROS
5th row0 A 10 LIBROS

Common Values

ValueCountFrequency (%)
26 A 100 LIBROS4842
31.4%
MÁS DE 100 LIBROS3272
21.2%
11 A 25 LIBROS3066
19.9%
0 A 10 LIBROS2492
16.1%
(Missing)1763
 
11.4%

Length

2021-12-11T12:33:13.544957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:13.619545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
libros13672
25.0%
a10400
19.0%
1008114
14.8%
264842
 
8.9%
más3272
 
6.0%
de3272
 
6.0%
113066
 
5.6%
253066
 
5.6%
02492
 
4.6%
102492
 
4.6%

Most occurring characters

ValueCountFrequency (%)
41016
20.1%
021212
10.4%
S16944
8.3%
116738
8.2%
B13672
 
6.7%
O13672
 
6.7%
L13672
 
6.7%
I13672
 
6.7%
R13672
 
6.7%
A10400
 
5.1%
Other values (7)28904
14.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter108792
53.4%
Decimal Number53766
26.4%
Space Separator41016
 
20.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S16944
15.6%
B13672
12.6%
O13672
12.6%
L13672
12.6%
I13672
12.6%
R13672
12.6%
A10400
9.6%
M3272
 
3.0%
Á3272
 
3.0%
D3272
 
3.0%
Decimal Number
ValueCountFrequency (%)
021212
39.5%
116738
31.1%
27908
 
14.7%
64842
 
9.0%
53066
 
5.7%
Space Separator
ValueCountFrequency (%)
41016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin108792
53.4%
Common94782
46.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S16944
15.6%
B13672
12.6%
O13672
12.6%
L13672
12.6%
I13672
12.6%
R13672
12.6%
A10400
9.6%
M3272
 
3.0%
Á3272
 
3.0%
D3272
 
3.0%
Common
ValueCountFrequency (%)
41016
43.3%
021212
22.4%
116738
17.7%
27908
 
8.3%
64842
 
5.1%
53066
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII200302
98.4%
None3272
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41016
20.5%
021212
10.6%
S16944
8.5%
116738
8.4%
B13672
 
6.8%
O13672
 
6.8%
L13672
 
6.8%
I13672
 
6.8%
R13672
 
6.8%
A10400
 
5.2%
Other values (6)25632
12.8%
None
ValueCountFrequency (%)
Á3272
100.0%

fami_comelechederivados
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1637
Missing (%)10.6%
Memory size120.7 KiB
Todos o casi todos los días
7615 
3 a 5 veces por semana
3180 
1 o 2 veces por semana
2401 
Nunca o rara vez comemos eso
 
602

Length

Max length28
Median length27
Mean length25.02123496
Min length22

Characters and Unicode

Total characters345243
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 o 2 veces por semana
2nd row1 o 2 veces por semana
3rd row1 o 2 veces por semana
4th rowTodos o casi todos los días
5th row1 o 2 veces por semana

Common Values

ValueCountFrequency (%)
Todos o casi todos los días7615
49.3%
3 a 5 veces por semana3180
20.6%
1 o 2 veces por semana2401
 
15.6%
Nunca o rara vez comemos eso602
 
3.9%
(Missing)1637
 
10.6%

Length

2021-12-11T12:33:13.864882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:13.937825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
todos15230
18.4%
o10618
12.8%
casi7615
9.2%
los7615
9.2%
días7615
9.2%
veces5581
 
6.7%
semana5581
 
6.7%
por5581
 
6.7%
53180
 
3.8%
a3180
 
3.8%
Other values (8)10992
13.3%

Most occurring characters

ValueCountFrequency (%)
68990
20.0%
o56080
16.2%
s50441
14.6%
a31378
9.1%
d22845
 
6.6%
e18549
 
5.4%
c14400
 
4.2%
T7615
 
2.2%
í7615
 
2.2%
t7615
 
2.2%
Other values (14)59715
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter256874
74.4%
Space Separator68990
 
20.0%
Decimal Number11162
 
3.2%
Uppercase Letter8217
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o56080
21.8%
s50441
19.6%
a31378
12.2%
d22845
8.9%
e18549
 
7.2%
c14400
 
5.6%
í7615
 
3.0%
t7615
 
3.0%
l7615
 
3.0%
i7615
 
3.0%
Other values (7)32721
12.7%
Decimal Number
ValueCountFrequency (%)
53180
28.5%
33180
28.5%
12401
21.5%
22401
21.5%
Uppercase Letter
ValueCountFrequency (%)
T7615
92.7%
N602
 
7.3%
Space Separator
ValueCountFrequency (%)
68990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin265091
76.8%
Common80152
 
23.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o56080
21.2%
s50441
19.0%
a31378
11.8%
d22845
8.6%
e18549
 
7.0%
c14400
 
5.4%
T7615
 
2.9%
í7615
 
2.9%
t7615
 
2.9%
l7615
 
2.9%
Other values (9)40938
15.4%
Common
ValueCountFrequency (%)
68990
86.1%
53180
 
4.0%
33180
 
4.0%
12401
 
3.0%
22401
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII337628
97.8%
None7615
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
68990
20.4%
o56080
16.6%
s50441
14.9%
a31378
9.3%
d22845
 
6.8%
e18549
 
5.5%
c14400
 
4.3%
T7615
 
2.3%
t7615
 
2.3%
l7615
 
2.3%
Other values (13)52100
15.4%
None
ValueCountFrequency (%)
í7615
100.0%

fami_comecarnepescadohuevo
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1535
Missing (%)9.9%
Memory size120.7 KiB
Todos o casi todos los días
9383 
3 a 5 veces por semana
2864 
1 o 2 veces por semana
1346 
Nunca o rara vez comemos eso
 
307

Length

Max length28
Median length27
Mean length25.50769784
Min length22

Characters and Unicode

Total characters354557
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 o 2 veces por semana
2nd row3 a 5 veces por semana
3rd rowTodos o casi todos los días
4th row3 a 5 veces por semana
5th rowNunca o rara vez comemos eso

Common Values

ValueCountFrequency (%)
Todos o casi todos los días9383
60.8%
3 a 5 veces por semana2864
 
18.6%
1 o 2 veces por semana1346
 
8.7%
Nunca o rara vez comemos eso307
 
2.0%
(Missing)1535
 
9.9%

Length

2021-12-11T12:33:14.028190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:14.097800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
todos18766
22.5%
o11036
13.2%
casi9383
11.3%
los9383
11.3%
días9383
11.3%
veces4210
 
5.0%
semana4210
 
5.0%
por4210
 
5.0%
52864
 
3.4%
a2864
 
3.4%
Other values (8)7091
 
8.5%

Most occurring characters

ValueCountFrequency (%)
69500
19.6%
o63082
17.8%
s55949
15.8%
a30971
8.7%
d28149
7.9%
c14207
 
4.0%
e13551
 
3.8%
T9383
 
2.6%
í9383
 
2.6%
t9383
 
2.6%
Other values (14)50999
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter266947
75.3%
Space Separator69500
 
19.6%
Uppercase Letter9690
 
2.7%
Decimal Number8420
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o63082
23.6%
s55949
21.0%
a30971
11.6%
d28149
10.5%
c14207
 
5.3%
e13551
 
5.1%
í9383
 
3.5%
t9383
 
3.5%
l9383
 
3.5%
i9383
 
3.5%
Other values (7)23506
 
8.8%
Decimal Number
ValueCountFrequency (%)
52864
34.0%
32864
34.0%
11346
16.0%
21346
16.0%
Uppercase Letter
ValueCountFrequency (%)
T9383
96.8%
N307
 
3.2%
Space Separator
ValueCountFrequency (%)
69500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin276637
78.0%
Common77920
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o63082
22.8%
s55949
20.2%
a30971
11.2%
d28149
10.2%
c14207
 
5.1%
e13551
 
4.9%
T9383
 
3.4%
í9383
 
3.4%
t9383
 
3.4%
l9383
 
3.4%
Other values (9)33196
12.0%
Common
ValueCountFrequency (%)
69500
89.2%
52864
 
3.7%
32864
 
3.7%
11346
 
1.7%
21346
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII345174
97.4%
None9383
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69500
20.1%
o63082
18.3%
s55949
16.2%
a30971
9.0%
d28149
8.2%
c14207
 
4.1%
e13551
 
3.9%
T9383
 
2.7%
t9383
 
2.7%
l9383
 
2.7%
Other values (13)41616
12.1%
None
ValueCountFrequency (%)
í9383
100.0%

fami_comecerealfrutoslegumbre
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1548
Missing (%)10.0%
Memory size120.7 KiB
3 a 5 veces por semana
4940 
Todos o casi todos los días
4679 
1 o 2 veces por semana
3483 
Nunca o rara vez comemos eso
785 

Length

Max length28
Median length22
Mean length24.02383524
Min length22

Characters and Unicode

Total characters333619
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTodos o casi todos los días
2nd row1 o 2 veces por semana
3rd row3 a 5 veces por semana
4th row1 o 2 veces por semana
5th rowNunca o rara vez comemos eso

Common Values

ValueCountFrequency (%)
3 a 5 veces por semana4940
32.0%
Todos o casi todos los días4679
30.3%
1 o 2 veces por semana3483
22.6%
Nunca o rara vez comemos eso785
 
5.1%
(Missing)1548
 
10.0%

Length

2021-12-11T12:33:14.191073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:14.263028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
todos9358
11.2%
o8947
10.7%
veces8423
10.1%
por8423
10.1%
semana8423
10.1%
34940
 
5.9%
a4940
 
5.9%
54940
 
5.9%
días4679
 
5.6%
los4679
 
5.6%
Other values (8)15570
18.7%

Most occurring characters

ValueCountFrequency (%)
69435
20.8%
o43120
12.9%
s41811
12.5%
a33499
10.0%
e27624
 
8.3%
c14672
 
4.4%
d14037
 
4.2%
r9993
 
3.0%
m9993
 
3.0%
n9208
 
2.8%
Other values (14)60227
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter241874
72.5%
Space Separator69435
 
20.8%
Decimal Number16846
 
5.0%
Uppercase Letter5464
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o43120
17.8%
s41811
17.3%
a33499
13.8%
e27624
11.4%
c14672
 
6.1%
d14037
 
5.8%
r9993
 
4.1%
m9993
 
4.1%
n9208
 
3.8%
v9208
 
3.8%
Other values (7)28709
11.9%
Decimal Number
ValueCountFrequency (%)
34940
29.3%
54940
29.3%
13483
20.7%
23483
20.7%
Uppercase Letter
ValueCountFrequency (%)
T4679
85.6%
N785
 
14.4%
Space Separator
ValueCountFrequency (%)
69435
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin247338
74.1%
Common86281
 
25.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o43120
17.4%
s41811
16.9%
a33499
13.5%
e27624
11.2%
c14672
 
5.9%
d14037
 
5.7%
r9993
 
4.0%
m9993
 
4.0%
n9208
 
3.7%
v9208
 
3.7%
Other values (9)34173
13.8%
Common
ValueCountFrequency (%)
69435
80.5%
34940
 
5.7%
54940
 
5.7%
13483
 
4.0%
23483
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII328940
98.6%
None4679
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69435
21.1%
o43120
13.1%
s41811
12.7%
a33499
10.2%
e27624
 
8.4%
c14672
 
4.5%
d14037
 
4.3%
r9993
 
3.0%
m9993
 
3.0%
n9208
 
2.8%
Other values (13)55548
16.9%
None
ValueCountFrequency (%)
í4679
100.0%

fami_situacioneconomica
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing1450
Missing (%)9.4%
Memory size120.7 KiB
Igual
8093 
Peor
3013 
Mejor
2879 

Length

Max length5
Median length5
Mean length4.78455488
Min length4

Characters and Unicode

Total characters66912
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMejor
2nd rowIgual
3rd rowIgual
4th rowIgual
5th rowPeor

Common Values

ValueCountFrequency (%)
Igual8093
52.4%
Peor3013
 
19.5%
Mejor2879
 
18.7%
(Missing)1450
 
9.4%

Length

2021-12-11T12:33:14.350454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:14.414490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
igual8093
57.9%
peor3013
 
21.5%
mejor2879
 
20.6%

Most occurring characters

ValueCountFrequency (%)
I8093
12.1%
g8093
12.1%
u8093
12.1%
a8093
12.1%
l8093
12.1%
e5892
8.8%
o5892
8.8%
r5892
8.8%
P3013
 
4.5%
M2879
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52927
79.1%
Uppercase Letter13985
 
20.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g8093
15.3%
u8093
15.3%
a8093
15.3%
l8093
15.3%
e5892
11.1%
o5892
11.1%
r5892
11.1%
j2879
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
I8093
57.9%
P3013
 
21.5%
M2879
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
Latin66912
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I8093
12.1%
g8093
12.1%
u8093
12.1%
a8093
12.1%
l8093
12.1%
e5892
8.8%
o5892
8.8%
r5892
8.8%
P3013
 
4.5%
M2879
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII66912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I8093
12.1%
g8093
12.1%
u8093
12.1%
a8093
12.1%
l8093
12.1%
e5892
8.8%
o5892
8.8%
r5892
8.8%
P3013
 
4.5%
M2879
 
4.3%

estu_dedicacionlecturadiaria
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing1532
Missing (%)9.9%
Memory size120.7 KiB
30 minutos o menos
4441 
Entre 30 y 60 minutos
3896 
No leo por entretenimiento
3263 
Entre 1 y 2 horas
1666 
Más de 2 horas
637 

Length

Max length26
Median length21
Mean length20.4151622
Min length14

Characters and Unicode

Total characters283832
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo leo por entretenimiento
2nd row30 minutos o menos
3rd row30 minutos o menos
4th rowNo leo por entretenimiento
5th rowEntre 30 y 60 minutos

Common Values

ValueCountFrequency (%)
30 minutos o menos4441
28.8%
Entre 30 y 60 minutos3896
25.2%
No leo por entretenimiento3263
21.1%
Entre 1 y 2 horas1666
 
10.8%
Más de 2 horas637
 
4.1%
(Missing)1532
 
9.9%

Length

2021-12-11T12:33:14.492106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:14.563743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
308337
13.6%
minutos8337
13.6%
entre5562
9.1%
y5562
9.1%
o4441
7.3%
menos4441
7.3%
603896
 
6.4%
no3263
 
5.3%
leo3263
 
5.3%
por3263
 
5.3%
Other values (6)10809
17.7%

Most occurring characters

ValueCountFrequency (%)
47271
16.7%
o32574
11.5%
n28129
9.9%
e26955
9.5%
t23688
8.3%
m16041
 
5.7%
s15718
 
5.5%
i14863
 
5.2%
r14391
 
5.1%
012233
 
4.3%
Other values (15)51969
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter198664
70.0%
Space Separator47271
 
16.7%
Decimal Number28435
 
10.0%
Uppercase Letter9462
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o32574
16.4%
n28129
14.2%
e26955
13.6%
t23688
11.9%
m16041
8.1%
s15718
7.9%
i14863
7.5%
r14391
7.2%
u8337
 
4.2%
y5562
 
2.8%
Other values (6)12406
 
6.2%
Decimal Number
ValueCountFrequency (%)
012233
43.0%
38337
29.3%
63896
 
13.7%
22303
 
8.1%
11666
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
E5562
58.8%
N3263
34.5%
M637
 
6.7%
Space Separator
ValueCountFrequency (%)
47271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin208126
73.3%
Common75706
 
26.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o32574
15.7%
n28129
13.5%
e26955
13.0%
t23688
11.4%
m16041
7.7%
s15718
7.6%
i14863
7.1%
r14391
6.9%
u8337
 
4.0%
E5562
 
2.7%
Other values (9)21868
10.5%
Common
ValueCountFrequency (%)
47271
62.4%
012233
 
16.2%
38337
 
11.0%
63896
 
5.1%
22303
 
3.0%
11666
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII283195
99.8%
None637
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47271
16.7%
o32574
11.5%
n28129
9.9%
e26955
9.5%
t23688
8.4%
m16041
 
5.7%
s15718
 
5.6%
i14863
 
5.2%
r14391
 
5.1%
012233
 
4.3%
Other values (14)51332
18.1%
None
ValueCountFrequency (%)
á637
100.0%

estu_dedicacioninternet
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing1553
Missing (%)10.1%
Memory size120.7 KiB
Más de 3 horas
5563 
Entre 1 y 3 horas
5040 
Entre 30 y 60 minutos
2061 
30 minutos o menos
914 
No Navega Internet
 
304

Length

Max length21
Median length17
Mean length16.47939778
Min length14

Characters and Unicode

Total characters228767
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Navega Internet
2nd rowNo Navega Internet
3rd rowEntre 1 y 3 horas
4th rowMás de 3 horas
5th rowMás de 3 horas

Common Values

ValueCountFrequency (%)
Más de 3 horas5563
36.0%
Entre 1 y 3 horas5040
32.7%
Entre 30 y 60 minutos2061
 
13.4%
30 minutos o menos914
 
5.9%
No Navega Internet304
 
2.0%
(Missing)1553
 
10.1%

Length

2021-12-11T12:33:14.662541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:14.735080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
310603
17.0%
horas10603
17.0%
entre7101
11.4%
y7101
11.4%
más5563
8.9%
de5563
8.9%
15040
8.1%
302975
 
4.8%
minutos2975
 
4.8%
602061
 
3.3%
Other values (5)2740
 
4.4%

Most occurring characters

ValueCountFrequency (%)
48443
21.2%
s20055
 
8.8%
r18008
 
7.9%
o15710
 
6.9%
e14490
 
6.3%
313578
 
5.9%
n11598
 
5.1%
a11211
 
4.9%
t10684
 
4.7%
h10603
 
4.6%
Other values (15)54387
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter141033
61.6%
Space Separator48443
 
21.2%
Decimal Number25715
 
11.2%
Uppercase Letter13576
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s20055
14.2%
r18008
12.8%
o15710
11.1%
e14490
10.3%
n11598
8.2%
a11211
7.9%
t10684
7.6%
h10603
7.5%
y7101
 
5.0%
á5563
 
3.9%
Other values (6)16010
11.4%
Decimal Number
ValueCountFrequency (%)
313578
52.8%
15040
 
19.6%
05036
 
19.6%
62061
 
8.0%
Uppercase Letter
ValueCountFrequency (%)
E7101
52.3%
M5563
41.0%
N608
 
4.5%
I304
 
2.2%
Space Separator
ValueCountFrequency (%)
48443
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin154609
67.6%
Common74158
32.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s20055
13.0%
r18008
11.6%
o15710
10.2%
e14490
9.4%
n11598
7.5%
a11211
 
7.3%
t10684
 
6.9%
h10603
 
6.9%
y7101
 
4.6%
E7101
 
4.6%
Other values (10)28048
18.1%
Common
ValueCountFrequency (%)
48443
65.3%
313578
 
18.3%
15040
 
6.8%
05036
 
6.8%
62061
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII223204
97.6%
None5563
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48443
21.7%
s20055
9.0%
r18008
 
8.1%
o15710
 
7.0%
e14490
 
6.5%
313578
 
6.1%
n11598
 
5.2%
a11211
 
5.0%
t10684
 
4.8%
h10603
 
4.8%
Other values (14)48824
21.9%
None
ValueCountFrequency (%)
á5563
100.0%

estu_horassemanatrabaja
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing1420
Missing (%)9.2%
Memory size120.7 KiB
0
9835 
Menos de 10 horas
1796 
Entre 11 y 20 horas
 
953
Más de 30 horas
 
915
Entre 21 y 30 horas
 
516

Length

Max length19
Median length1
Mean length5.85108812
Min length1

Characters and Unicode

Total characters82003
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMás de 30 horas
2nd rowEntre 11 y 20 horas
3rd rowEntre 11 y 20 horas
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09835
63.7%
Menos de 10 horas1796
 
11.6%
Entre 11 y 20 horas953
 
6.2%
Más de 30 horas915
 
5.9%
Entre 21 y 30 horas516
 
3.3%
(Missing)1420
 
9.2%

Length

2021-12-11T12:33:14.828084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:14.898333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
09835
35.1%
horas4180
14.9%
de2711
 
9.7%
menos1796
 
6.4%
101796
 
6.4%
entre1469
 
5.2%
y1469
 
5.2%
301431
 
5.1%
11953
 
3.4%
20953
 
3.4%
Other values (2)1431
 
5.1%

Most occurring characters

ValueCountFrequency (%)
014015
17.1%
14009
17.1%
s6891
8.4%
e5976
7.3%
o5976
7.3%
r5649
6.9%
14218
 
5.1%
a4180
 
5.1%
h4180
 
5.1%
n3265
 
4.0%
Other values (8)13644
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter42681
52.0%
Decimal Number21133
25.8%
Space Separator14009
 
17.1%
Uppercase Letter4180
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s6891
16.1%
e5976
14.0%
o5976
14.0%
r5649
13.2%
a4180
9.8%
h4180
9.8%
n3265
7.6%
d2711
 
6.4%
t1469
 
3.4%
y1469
 
3.4%
Decimal Number
ValueCountFrequency (%)
014015
66.3%
14218
 
20.0%
21469
 
7.0%
31431
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
M2711
64.9%
E1469
35.1%
Space Separator
ValueCountFrequency (%)
14009
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin46861
57.1%
Common35142
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s6891
14.7%
e5976
12.8%
o5976
12.8%
r5649
12.1%
a4180
8.9%
h4180
8.9%
n3265
7.0%
M2711
 
5.8%
d2711
 
5.8%
E1469
 
3.1%
Other values (3)3853
8.2%
Common
ValueCountFrequency (%)
014015
39.9%
14009
39.9%
14218
 
12.0%
21469
 
4.2%
31431
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII81088
98.9%
None915
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014015
17.3%
14009
17.3%
s6891
8.5%
e5976
7.4%
o5976
7.4%
r5649
7.0%
14218
 
5.2%
a4180
 
5.2%
h4180
 
5.2%
n3265
 
4.0%
Other values (7)12729
15.7%
None
ValueCountFrequency (%)
á915
100.0%

estu_tiporemuneracion
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1458
Missing (%)9.4%
Memory size120.7 KiB
No
10220 
Si, en efectivo
3325 
Si, en especie
 
226
Si, en efectivo y especie
 
206

Length

Max length25
Median length2
Mean length5.625599199
Min length2

Characters and Unicode

Total characters78629
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi, en efectivo
2nd rowSi, en efectivo
3rd rowSi, en efectivo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No10220
66.2%
Si, en efectivo3325
 
21.5%
Si, en especie226
 
1.5%
Si, en efectivo y especie206
 
1.3%
(Missing)1458
 
9.4%

Length

2021-12-11T12:33:14.987105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:15.050252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no10220
46.7%
si3757
 
17.2%
en3757
 
17.2%
efectivo3531
 
16.1%
especie432
 
2.0%
y206
 
0.9%

Most occurring characters

ValueCountFrequency (%)
o13751
17.5%
e12115
15.4%
N10220
13.0%
7926
10.1%
i7720
9.8%
c3963
 
5.0%
S3757
 
4.8%
,3757
 
4.8%
n3757
 
4.8%
f3531
 
4.5%
Other values (5)8132
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52969
67.4%
Uppercase Letter13977
 
17.8%
Space Separator7926
 
10.1%
Other Punctuation3757
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o13751
26.0%
e12115
22.9%
i7720
14.6%
c3963
 
7.5%
n3757
 
7.1%
f3531
 
6.7%
t3531
 
6.7%
v3531
 
6.7%
s432
 
0.8%
p432
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
N10220
73.1%
S3757
 
26.9%
Space Separator
ValueCountFrequency (%)
7926
100.0%
Other Punctuation
ValueCountFrequency (%)
,3757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin66946
85.1%
Common11683
 
14.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o13751
20.5%
e12115
18.1%
N10220
15.3%
i7720
11.5%
c3963
 
5.9%
S3757
 
5.6%
n3757
 
5.6%
f3531
 
5.3%
t3531
 
5.3%
v3531
 
5.3%
Other values (3)1070
 
1.6%
Common
ValueCountFrequency (%)
7926
67.8%
,3757
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII78629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o13751
17.5%
e12115
15.4%
N10220
13.0%
7926
10.1%
i7720
9.8%
c3963
 
5.0%
S3757
 
4.8%
,3757
 
4.8%
n3757
 
4.8%
f3531
 
4.5%
Other values (5)8132
10.3%

cole_codigo_icfes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct667
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130832.5166
Minimum182
Maximum730416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:15.145915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum182
5-th percentile16691
Q124299
median79327
Q3135574
95-th percentile662742
Maximum730416
Range730234
Interquartile range (IQR)111275

Descriptive statistics

Standard deviation178352.1596
Coefficient of variation (CV)1.363209731
Kurtosis5.016069247
Mean130832.5166
Median Absolute Deviation (MAD)55588
Skewness2.4733045
Sum2019399893
Variance3.180949285 × 1010
MonotonicityNot monotonic
2021-12-11T12:33:15.275516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58727186
 
1.2%
156026139
 
0.9%
16691130
 
0.8%
17293129
 
0.8%
17335118
 
0.8%
95869115
 
0.7%
84988110
 
0.7%
36681109
 
0.7%
70094106
 
0.7%
17228106
 
0.7%
Other values (657)14187
91.9%
ValueCountFrequency (%)
18266
0.4%
37249
0.3%
59625
 
0.2%
245132
0.2%
304644
0.3%
332717
 
0.1%
341814
 
0.1%
423443
0.3%
602359
0.4%
701352
0.3%
ValueCountFrequency (%)
7304162
 
< 0.1%
7301763
 
< 0.1%
7301689
 
0.1%
72999618
 
0.1%
7299476
 
< 0.1%
72993952
0.3%
7299051
 
< 0.1%
7298302
 
< 0.1%
7296248
 
0.1%
7275602
 
< 0.1%

cole_cod_dane_establecimiento
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct606
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.498385072 × 1011
Minimum1.050450003 × 1011
Maximum6.252690001 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:15.406406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.050450003 × 1011
5-th percentile3.050450017 × 1011
Q13.117690009 × 1011
median3.680010001 × 1011
Q33.760010265 × 1011
95-th percentile4.251750323 × 1011
Maximum6.252690001 × 1011
Range5.202239998 × 1011
Interquartile range (IQR)6.42320256 × 1010

Descriptive statistics

Standard deviation4.704430848 × 1010
Coefficient of variation (CV)0.1344743575
Kurtosis4.66559759
Mean3.498385072 × 1011
Median Absolute Deviation (MAD)4.224699476 × 1010
Skewness-0.4505357314
Sum5.399757358 × 1015
Variance2.21316696 × 1021
MonotonicityNot monotonic
2021-12-11T12:33:15.536093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.760010282 × 1011186
 
1.2%
3.760010077 × 1011149
 
1.0%
3.760010358 × 1011139
 
0.9%
3.760010011 × 1011130
 
0.8%
3.760010035 × 1011129
 
0.8%
3.760010003 × 1011118
 
0.8%
3.050010227 × 1011115
 
0.7%
3.760010269 × 1011109
 
0.7%
3.760010009 × 1011106
 
0.7%
3.760010003 × 1011106
 
0.7%
Other values (596)14148
91.7%
ValueCountFrequency (%)
1.050450003 × 10112
 
< 0.1%
1.152380009 × 101115
0.1%
1.170010007 × 10113
 
< 0.1%
1.173800001 × 101110
 
0.1%
1.19001004 × 10113
 
< 0.1%
1.207500004 × 101129
0.2%
1.25513001 × 10118
 
0.1%
1.255130011 × 101118
0.1%
1.448550005 × 10111
 
< 0.1%
1.500010027 × 101114
0.1%
ValueCountFrequency (%)
6.252690001 × 10114
 
< 0.1%
5.19001 × 101145
0.3%
4.768921001 × 10117
 
< 0.1%
4.768920997 × 10113
 
< 0.1%
4.768920997 × 101116
 
0.1%
4.765200072 × 101148
0.3%
4.765200063 × 101118
 
0.1%
4.765200038 × 101115
 
0.1%
4.76248001 × 10117
 
< 0.1%
4.762480005 × 101150
0.3%

cole_nombre_establecimiento
Categorical

HIGH CARDINALITY

Distinct592
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
COLEGIO COMFANDI EL PRADO
 
186
COLEGIO LEON DE GREIFF
 
149
COLEGIO MAYOR SAN FRANCISCO DE ASIS
 
139
COLEGIO AMERICANO
 
130
COLEGIO SAN ANTONIO MARIA CLARET
 
129
Other values (587)
14702 

Length

Max length74
Median length26
Mean length27.12089407
Min length6

Characters and Unicode

Total characters418611
Distinct characters49
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.2%

Sample

1st rowI.E. MANUEL RODRIGUEZ TORICES
2nd rowINSTITUTO DE EDUCACIÓN TECNICA INESUR
3rd rowCOORPORACION EDUCATIVA DEL SUR OCCIDENTE COLOMBIANO
4th rowNUEVO INSTITUTO DE APRENDIZAJE SURCOLOMBIANO
5th rowCOL FUNDACION LIC INGLES

Common Values

ValueCountFrequency (%)
COLEGIO COMFANDI EL PRADO186
 
1.2%
COLEGIO LEON DE GREIFF149
 
1.0%
COLEGIO MAYOR SAN FRANCISCO DE ASIS139
 
0.9%
COLEGIO AMERICANO130
 
0.8%
COLEGIO SAN ANTONIO MARIA CLARET129
 
0.8%
COL BERCHMANS118
 
0.8%
POLITÉCNICO MAYOR AGENCIA CRISTIANA DE SERVICIO Y EDUCACIÓN115
 
0.7%
COMFANDI MIRAFLORES109
 
0.7%
COL PARROQ SAN JUAN BAUTISTA106
 
0.7%
COLEGIO PARROQIAL SANTIAGO APOSTOL106
 
0.7%
Other values (582)14148
91.7%

Length

2021-12-11T12:33:15.680707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
colegio6190
 
9.9%
col4178
 
6.7%
de3879
 
6.2%
san1835
 
2.9%
la1013
 
1.6%
los889
 
1.4%
del852
 
1.4%
instituto700
 
1.1%
gimnasio687
 
1.1%
comfandi576
 
0.9%
Other values (837)41844
66.8%

Most occurring characters

ValueCountFrequency (%)
47774
11.4%
O44456
10.6%
A39682
9.5%
E35603
 
8.5%
I35076
 
8.4%
C30251
 
7.2%
L27929
 
6.7%
N27079
 
6.5%
S20312
 
4.9%
R19504
 
4.7%
Other values (39)90945
21.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter367956
87.9%
Space Separator47774
 
11.4%
Other Punctuation1243
 
0.3%
Decimal Number708
 
0.2%
Dash Punctuation476
 
0.1%
Open Punctuation227
 
0.1%
Close Punctuation227
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O44456
12.1%
A39682
10.8%
E35603
9.7%
I35076
9.5%
C30251
8.2%
L27929
 
7.6%
N27079
 
7.4%
S20312
 
5.5%
R19504
 
5.3%
T16541
 
4.5%
Other values (23)71523
19.4%
Decimal Number
ValueCountFrequency (%)
0395
55.8%
2182
25.7%
171
 
10.0%
732
 
4.5%
57
 
1.0%
37
 
1.0%
47
 
1.0%
87
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.1056
85.0%
"164
 
13.2%
¿18
 
1.4%
&5
 
0.4%
Space Separator
ValueCountFrequency (%)
47774
100.0%
Dash Punctuation
ValueCountFrequency (%)
-476
100.0%
Open Punctuation
ValueCountFrequency (%)
(227
100.0%
Close Punctuation
ValueCountFrequency (%)
)227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin367956
87.9%
Common50655
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O44456
12.1%
A39682
10.8%
E35603
9.7%
I35076
9.5%
C30251
8.2%
L27929
 
7.6%
N27079
 
7.4%
S20312
 
5.5%
R19504
 
5.3%
T16541
 
4.5%
Other values (23)71523
19.4%
Common
ValueCountFrequency (%)
47774
94.3%
.1056
 
2.1%
-476
 
0.9%
0395
 
0.8%
(227
 
0.4%
)227
 
0.4%
2182
 
0.4%
"164
 
0.3%
171
 
0.1%
732
 
0.1%
Other values (6)51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII416294
99.4%
None2317
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47774
11.5%
O44456
10.7%
A39682
9.5%
E35603
8.6%
I35076
 
8.4%
C30251
 
7.3%
L27929
 
6.7%
N27079
 
6.5%
S20312
 
4.9%
R19504
 
4.7%
Other values (31)88628
21.3%
None
ValueCountFrequency (%)
Ñ650
28.1%
Ó500
21.6%
É326
14.1%
Í284
12.3%
Ü249
 
10.7%
Á198
 
8.5%
Ú92
 
4.0%
¿18
 
0.8%

cole_genero
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
MIXTO
13933 
FEMENINO
 
1025
MASCULINO
 
477

Length

Max length9
Median length5
Mean length5.322837707
Min length5

Characters and Unicode

Total characters82158
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIXTO
2nd rowMIXTO
3rd rowMIXTO
4th rowMIXTO
5th rowMIXTO

Common Values

ValueCountFrequency (%)
MIXTO13933
90.3%
FEMENINO1025
 
6.6%
MASCULINO477
 
3.1%

Length

2021-12-11T12:33:15.930007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:16.008187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
mixto13933
90.3%
femenino1025
 
6.6%
masculino477
 
3.1%

Most occurring characters

ValueCountFrequency (%)
M15435
18.8%
I15435
18.8%
O15435
18.8%
X13933
17.0%
T13933
17.0%
N2527
 
3.1%
E2050
 
2.5%
F1025
 
1.2%
A477
 
0.6%
S477
 
0.6%
Other values (3)1431
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter82158
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M15435
18.8%
I15435
18.8%
O15435
18.8%
X13933
17.0%
T13933
17.0%
N2527
 
3.1%
E2050
 
2.5%
F1025
 
1.2%
A477
 
0.6%
S477
 
0.6%
Other values (3)1431
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin82158
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M15435
18.8%
I15435
18.8%
O15435
18.8%
X13933
17.0%
T13933
17.0%
N2527
 
3.1%
E2050
 
2.5%
F1025
 
1.2%
A477
 
0.6%
S477
 
0.6%
Other values (3)1431
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII82158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M15435
18.8%
I15435
18.8%
O15435
18.8%
X13933
17.0%
T13933
17.0%
N2527
 
3.1%
E2050
 
2.5%
F1025
 
1.2%
A477
 
0.6%
S477
 
0.6%
Other values (3)1431
 
1.7%

cole_naturaleza
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
NO OFICIAL
15217 
OFICIAL
 
218

Length

Max length10
Median length10
Mean length9.957628766
Min length7

Characters and Unicode

Total characters153696
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOFICIAL
2nd rowNO OFICIAL
3rd rowNO OFICIAL
4th rowNO OFICIAL
5th rowNO OFICIAL

Common Values

ValueCountFrequency (%)
NO OFICIAL15217
98.6%
OFICIAL218
 
1.4%

Length

2021-12-11T12:33:16.085970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:16.160071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
oficial15435
50.4%
no15217
49.6%

Most occurring characters

ValueCountFrequency (%)
I30870
20.1%
O30652
19.9%
F15435
10.0%
C15435
10.0%
A15435
10.0%
L15435
10.0%
N15217
9.9%
15217
9.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter138479
90.1%
Space Separator15217
 
9.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I30870
22.3%
O30652
22.1%
F15435
11.1%
C15435
11.1%
A15435
11.1%
L15435
11.1%
N15217
11.0%
Space Separator
ValueCountFrequency (%)
15217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin138479
90.1%
Common15217
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I30870
22.3%
O30652
22.1%
F15435
11.1%
C15435
11.1%
A15435
11.1%
L15435
11.1%
N15217
11.0%
Common
ValueCountFrequency (%)
15217
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII153696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I30870
20.1%
O30652
19.9%
F15435
10.0%
C15435
10.0%
A15435
10.0%
L15435
10.0%
N15217
9.9%
15217
9.9%

cole_calendario
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
B
11865 
A
3055 
OTRO
 
515

Length

Max length4
Median length1
Mean length1.100097182
Min length1

Characters and Unicode

Total characters16980
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowOTRO
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
B11865
76.9%
A3055
 
19.8%
OTRO515
 
3.3%

Length

2021-12-11T12:33:16.233873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:16.306156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
b11865
76.9%
a3055
 
19.8%
otro515
 
3.3%

Most occurring characters

ValueCountFrequency (%)
B11865
69.9%
A3055
 
18.0%
O1030
 
6.1%
T515
 
3.0%
R515
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter16980
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B11865
69.9%
A3055
 
18.0%
O1030
 
6.1%
T515
 
3.0%
R515
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16980
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B11865
69.9%
A3055
 
18.0%
O1030
 
6.1%
T515
 
3.0%
R515
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B11865
69.9%
A3055
 
18.0%
O1030
 
6.1%
T515
 
3.0%
R515
 
3.0%

cole_bilingue
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing2615
Missing (%)16.9%
Memory size120.7 KiB
N
9769 
S
3051 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12820
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N9769
63.3%
S3051
 
19.8%
(Missing)2615
 
16.9%

Length

2021-12-11T12:33:16.374924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:16.436696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
n9769
76.2%
s3051
 
23.8%

Most occurring characters

ValueCountFrequency (%)
N9769
76.2%
S3051
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter12820
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N9769
76.2%
S3051
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Latin12820
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N9769
76.2%
S3051
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII12820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N9769
76.2%
S3051
 
23.8%

cole_caracter
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing928
Missing (%)6.0%
Memory size120.7 KiB
ACADÉMICO
11723 
TÉCNICO
1607 
TÉCNICO/ACADÉMICO
 
1099
NO APLICA
 
78

Length

Max length17
Median length9
Mean length9.384504033
Min length7

Characters and Unicode

Total characters136141
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTÉCNICO/ACADÉMICO
2nd rowACADÉMICO
3rd rowTÉCNICO/ACADÉMICO
4th rowACADÉMICO
5th rowTÉCNICO/ACADÉMICO

Common Values

ValueCountFrequency (%)
ACADÉMICO11723
76.0%
TÉCNICO1607
 
10.4%
TÉCNICO/ACADÉMICO1099
 
7.1%
NO APLICA78
 
0.5%
(Missing)928
 
6.0%

Length

2021-12-11T12:33:16.502984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:16.568015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
académico11723
80.4%
técnico1607
 
11.0%
técnico/académico1099
 
7.5%
no78
 
0.5%
aplica78
 
0.5%

Most occurring characters

ValueCountFrequency (%)
C31134
22.9%
A25800
19.0%
I15606
11.5%
O15606
11.5%
É15528
11.4%
D12822
9.4%
M12822
9.4%
N2784
 
2.0%
T2706
 
2.0%
/1099
 
0.8%
Other values (3)234
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter134964
99.1%
Other Punctuation1099
 
0.8%
Space Separator78
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C31134
23.1%
A25800
19.1%
I15606
11.6%
O15606
11.6%
É15528
11.5%
D12822
9.5%
M12822
9.5%
N2784
 
2.1%
T2706
 
2.0%
P78
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/1099
100.0%
Space Separator
ValueCountFrequency (%)
78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin134964
99.1%
Common1177
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
C31134
23.1%
A25800
19.1%
I15606
11.6%
O15606
11.6%
É15528
11.5%
D12822
9.5%
M12822
9.5%
N2784
 
2.1%
T2706
 
2.0%
P78
 
0.1%
Common
ValueCountFrequency (%)
/1099
93.4%
78
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII120613
88.6%
None15528
 
11.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C31134
25.8%
A25800
21.4%
I15606
12.9%
O15606
12.9%
D12822
10.6%
M12822
10.6%
N2784
 
2.3%
T2706
 
2.2%
/1099
 
0.9%
78
 
0.1%
Other values (2)156
 
0.1%
None
ValueCountFrequency (%)
É15528
100.0%

cole_cod_dane_sede
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct606
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.49838538 × 1011
Minimum1.050450003 × 1011
Maximum6.252690001 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:16.670465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.050450003 × 1011
5-th percentile3.050450017 × 1011
Q13.117690009 × 1011
median3.680010001 × 1011
Q33.760010265 × 1011
95-th percentile4.251750323 × 1011
Maximum6.252690001 × 1011
Range5.202239998 × 1011
Interquartile range (IQR)6.42320256 × 1010

Descriptive statistics

Standard deviation4.704428574 × 1010
Coefficient of variation (CV)0.1344742807
Kurtosis4.66561254
Mean3.49838538 × 1011
Median Absolute Deviation (MAD)4.224699476 × 1010
Skewness-0.4505372796
Sum5.399757834 × 1015
Variance2.213164821 × 1021
MonotonicityNot monotonic
2021-12-11T12:33:16.802602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.760010282 × 1011186
 
1.2%
3.760010077 × 1011149
 
1.0%
3.760010358 × 1011139
 
0.9%
3.760010011 × 1011130
 
0.8%
3.760010035 × 1011129
 
0.8%
3.760010003 × 1011118
 
0.8%
3.050010227 × 1011115
 
0.7%
3.760010269 × 1011109
 
0.7%
3.760010009 × 1011106
 
0.7%
3.760010003 × 1011106
 
0.7%
Other values (596)14148
91.7%
ValueCountFrequency (%)
1.050450003 × 10112
 
< 0.1%
1.152380009 × 101115
0.1%
1.170010007 × 10113
 
< 0.1%
1.173800001 × 101110
 
0.1%
1.19001004 × 10113
 
< 0.1%
1.207500004 × 101129
0.2%
1.25513001 × 10118
 
0.1%
1.255130011 × 101118
0.1%
1.448550005 × 10111
 
< 0.1%
1.500010027 × 101114
0.1%
ValueCountFrequency (%)
6.252690001 × 10114
 
< 0.1%
5.19001 × 101145
0.3%
4.768921001 × 10117
 
< 0.1%
4.768920997 × 10113
 
< 0.1%
4.768920997 × 101116
 
0.1%
4.765200072 × 101148
0.3%
4.765200063 × 101118
 
0.1%
4.765200038 × 101115
 
0.1%
4.76248001 × 10117
 
< 0.1%
4.762480005 × 101150
0.3%

cole_nombre_sede
Categorical

HIGH CARDINALITY

Distinct599
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
COLEGIO COMFANDI EL PRADO
 
186
COLEGIO LEON DE GREIFF
 
149
COLEGIO MAYOR SAN FRANCISCO DE ASIS - SEDE PRINCIPAL
 
139
COLEGIO AMERICANO
 
130
COLEGIO SAN ANTONIO MARIA CLARET
 
129
Other values (594)
14702 

Length

Max length86
Median length28
Mean length30.73683188
Min length7

Characters and Unicode

Total characters474423
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.2%

Sample

1st rowI.E. MANUEL RODRIGUEZ TORICES
2nd rowINSTITUTO DE EDUCACIÓN TECNICA INESUR - SEDE PRINCIPAL
3rd rowCOORPORACION EDUCATIVA DEL SUR OCCIDENTE COLOMBIANO - SEDE PRINCIPAL
4th rowNUEVO INSTITUTO DE APRENDIZAJE SURCOLOMBIANO - SEDE PRINCIPAL
5th rowCOL FUNDACION LIC INGLES

Common Values

ValueCountFrequency (%)
COLEGIO COMFANDI EL PRADO186
 
1.2%
COLEGIO LEON DE GREIFF149
 
1.0%
COLEGIO MAYOR SAN FRANCISCO DE ASIS - SEDE PRINCIPAL139
 
0.9%
COLEGIO AMERICANO130
 
0.8%
COLEGIO SAN ANTONIO MARIA CLARET129
 
0.8%
COL BERCHMANS118
 
0.8%
POLITÉCNICO MAYOR AGENCIA CRISTIANA DE SERVICIO Y EDUCACIÓN115
 
0.7%
COMFANDI MIRAFLORES109
 
0.7%
COLEGIO PARROQIAL SANTIAGO APOSTOL106
 
0.7%
COL PARROQ SAN JUAN BAUTISTA106
 
0.7%
Other values (589)14148
91.7%

Length

2021-12-11T12:33:16.948863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
colegio5985
 
8.2%
col4360
 
6.0%
de3883
 
5.3%
3827
 
5.3%
sede3645
 
5.0%
principal3393
 
4.7%
san1848
 
2.5%
la1013
 
1.4%
los889
 
1.2%
del852
 
1.2%
Other values (842)43073
59.2%

Most occurring characters

ValueCountFrequency (%)
58000
12.2%
O44044
9.3%
A42733
9.0%
E41832
8.8%
I41640
 
8.8%
C33575
 
7.1%
L31189
 
6.6%
N30656
 
6.5%
S23516
 
5.0%
R22866
 
4.8%
Other values (50)104372
22.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter410072
86.4%
Space Separator58000
 
12.2%
Dash Punctuation3889
 
0.8%
Other Punctuation1311
 
0.3%
Decimal Number708
 
0.1%
Open Punctuation215
 
< 0.1%
Close Punctuation215
 
< 0.1%
Lowercase Letter13
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O44044
10.7%
A42733
10.4%
E41832
10.2%
I41640
10.2%
C33575
8.2%
L31189
 
7.6%
N30656
 
7.5%
S23516
 
5.7%
R22866
 
5.6%
T16396
 
4.0%
Other values (23)81625
19.9%
Lowercase Letter
ValueCountFrequency (%)
e2
15.4%
p2
15.4%
i2
15.4%
s1
7.7%
d1
7.7%
r1
7.7%
n1
7.7%
c1
7.7%
a1
7.7%
l1
7.7%
Decimal Number
ValueCountFrequency (%)
0395
55.8%
2182
25.7%
171
 
10.0%
732
 
4.5%
47
 
1.0%
87
 
1.0%
37
 
1.0%
57
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.1064
81.2%
"180
 
13.7%
,32
 
2.4%
/30
 
2.3%
&5
 
0.4%
Space Separator
ValueCountFrequency (%)
58000
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3889
100.0%
Open Punctuation
ValueCountFrequency (%)
(215
100.0%
Close Punctuation
ValueCountFrequency (%)
)215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin410085
86.4%
Common64338
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
O44044
10.7%
A42733
10.4%
E41832
10.2%
I41640
10.2%
C33575
8.2%
L31189
 
7.6%
N30656
 
7.5%
S23516
 
5.7%
R22866
 
5.6%
T16396
 
4.0%
Other values (33)81638
19.9%
Common
ValueCountFrequency (%)
58000
90.1%
-3889
 
6.0%
.1064
 
1.7%
0395
 
0.6%
(215
 
0.3%
)215
 
0.3%
2182
 
0.3%
"180
 
0.3%
171
 
0.1%
,32
 
< 0.1%
Other values (7)95
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII472063
99.5%
None2360
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58000
12.3%
O44044
9.3%
A42733
9.1%
E41832
8.9%
I41640
8.8%
C33575
 
7.1%
L31189
 
6.6%
N30656
 
6.5%
S23516
 
5.0%
R22866
 
4.8%
Other values (43)102012
21.6%
None
ValueCountFrequency (%)
Ñ661
28.0%
Ó573
24.3%
É323
13.7%
Í284
12.0%
Ü231
 
9.8%
Á196
 
8.3%
Ú92
 
3.9%

cole_sede_principal
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
S
15426 
N
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15435
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S15426
99.9%
N9
 
0.1%

Length

2021-12-11T12:33:17.070392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:17.134475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
s15426
99.9%
n9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S15426
99.9%
N9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter15435
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S15426
99.9%
N9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin15435
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S15426
99.9%
N9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S15426
99.9%
N9
 
0.1%

cole_area_ubicacion
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
URBANO
13503 
RURAL
1932 

Length

Max length6
Median length6
Mean length5.874829932
Min length5

Characters and Unicode

Total characters90678
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURBANO
2nd rowURBANO
3rd rowURBANO
4th rowURBANO
5th rowRURAL

Common Values

ValueCountFrequency (%)
URBANO13503
87.5%
RURAL1932
 
12.5%

Length

2021-12-11T12:33:17.202796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:17.267333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
urbano13503
87.5%
rural1932
 
12.5%

Most occurring characters

ValueCountFrequency (%)
R17367
19.2%
U15435
17.0%
A15435
17.0%
B13503
14.9%
N13503
14.9%
O13503
14.9%
L1932
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter90678
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R17367
19.2%
U15435
17.0%
A15435
17.0%
B13503
14.9%
N13503
14.9%
O13503
14.9%
L1932
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin90678
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R17367
19.2%
U15435
17.0%
A15435
17.0%
B13503
14.9%
N13503
14.9%
O13503
14.9%
L1932
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII90678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R17367
19.2%
U15435
17.0%
A15435
17.0%
B13503
14.9%
N13503
14.9%
O13503
14.9%
L1932
 
2.1%

cole_jornada
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
COMPLETA
7727 
MAÑANA
5041 
SABATINA
1176 
NOCHE
946 
TARDE
 
350

Length

Max length8
Median length8
Mean length7.057013282
Min length5

Characters and Unicode

Total characters108925
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOCHE
2nd rowSABATINA
3rd rowMAÑANA
4th rowSABATINA
5th rowCOMPLETA

Common Values

ValueCountFrequency (%)
COMPLETA7727
50.1%
MAÑANA5041
32.7%
SABATINA1176
 
7.6%
NOCHE946
 
6.1%
TARDE350
 
2.3%
UNICA195
 
1.3%

Length

2021-12-11T12:33:17.333271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:17.401250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
completa7727
50.1%
mañana5041
32.7%
sabatina1176
 
7.6%
noche946
 
6.1%
tarde350
 
2.3%
unica195
 
1.3%

Most occurring characters

ValueCountFrequency (%)
A26923
24.7%
M12768
11.7%
T9253
 
8.5%
E9023
 
8.3%
C8868
 
8.1%
O8673
 
8.0%
P7727
 
7.1%
L7727
 
7.1%
N7358
 
6.8%
Ñ5041
 
4.6%
Other values (7)5564
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter108925
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A26923
24.7%
M12768
11.7%
T9253
 
8.5%
E9023
 
8.3%
C8868
 
8.1%
O8673
 
8.0%
P7727
 
7.1%
L7727
 
7.1%
N7358
 
6.8%
Ñ5041
 
4.6%
Other values (7)5564
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin108925
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A26923
24.7%
M12768
11.7%
T9253
 
8.5%
E9023
 
8.3%
C8868
 
8.1%
O8673
 
8.0%
P7727
 
7.1%
L7727
 
7.1%
N7358
 
6.8%
Ñ5041
 
4.6%
Other values (7)5564
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII103884
95.4%
None5041
 
4.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A26923
25.9%
M12768
12.3%
T9253
 
8.9%
E9023
 
8.7%
C8868
 
8.5%
O8673
 
8.3%
P7727
 
7.4%
L7727
 
7.4%
N7358
 
7.1%
I1371
 
1.3%
Other values (6)4193
 
4.0%
None
ValueCountFrequency (%)
Ñ5041
100.0%

cole_cod_mcpio_ubicacion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct103
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44475.56482
Minimum5001
Maximum86320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:17.500390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile8001
Q111001
median52001
Q376001
95-th percentile76520
Maximum86320
Range81319
Interquartile range (IQR)65000

Descriptive statistics

Standard deviation30386.87119
Coefficient of variation (CV)0.683226201
Kurtosis-1.863879746
Mean44475.56482
Median Absolute Deviation (MAD)24519
Skewness-0.04998249833
Sum686480343
Variance923361940.7
MonotonicityNot monotonic
2021-12-11T12:33:17.627961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
760015082
32.9%
110013610
23.4%
19001645
 
4.2%
76520591
 
3.8%
52001407
 
2.6%
5001380
 
2.5%
25175379
 
2.5%
8573371
 
2.4%
76111309
 
2.0%
66001212
 
1.4%
Other values (93)3449
22.3%
ValueCountFrequency (%)
5001380
2.5%
504516
 
0.1%
5266155
1.0%
53609
 
0.1%
537610
 
0.1%
538082
 
0.5%
560727
 
0.2%
563132
 
0.2%
56974
 
< 0.1%
8001190
1.2%
ValueCountFrequency (%)
863201
 
< 0.1%
8600143
 
0.3%
850018
 
0.1%
8100111
 
0.1%
76892182
 
1.2%
7662221
 
0.1%
7656331
 
0.2%
76520591
3.8%
76364102
 
0.7%
762751
 
< 0.1%

cole_mcpio_ubicacion
Categorical

HIGH CARDINALITY

Distinct103
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
CALI
5082 
BOGOTÁ D.C.
3610 
POPAYÁN
645 
PALMIRA
591 
PASTO
 
407
Other values (98)
5100 

Length

Max length27
Median length7
Mean length7.71350826
Min length4

Characters and Unicode

Total characters119058
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowSAN DIEGO
2nd rowIPIALES
3rd rowPOPAYÁN
4th rowMOCOA
5th rowPEREIRA

Common Values

ValueCountFrequency (%)
CALI5082
32.9%
BOGOTÁ D.C.3610
23.4%
POPAYÁN645
 
4.2%
PALMIRA591
 
3.8%
PASTO407
 
2.6%
MEDELLÍN380
 
2.5%
CHÍA379
 
2.5%
PUERTO COLOMBIA371
 
2.4%
GUADALAJARA DE BUGA309
 
2.0%
PEREIRA212
 
1.4%
Other values (93)3449
22.3%

Length

2021-12-11T12:33:17.763058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cali5082
24.1%
d.c3610
17.2%
bogotá3610
17.2%
popayán645
 
3.1%
palmira591
 
2.8%
de541
 
2.6%
puerto433
 
2.1%
pasto407
 
1.9%
medellín380
 
1.8%
chía379
 
1.8%
Other values (107)5368
25.5%

Most occurring characters

ValueCountFrequency (%)
A16414
13.8%
C11291
 
9.5%
O10927
 
9.2%
L9306
 
7.8%
I8570
 
7.2%
.7220
 
6.1%
D5830
 
4.9%
T5728
 
4.8%
5611
 
4.7%
B5013
 
4.2%
Other values (22)33148
27.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter106227
89.2%
Other Punctuation7220
 
6.1%
Space Separator5611
 
4.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A16414
15.5%
C11291
10.6%
O10927
10.3%
L9306
 
8.8%
I8570
 
8.1%
D5830
 
5.5%
T5728
 
5.4%
B5013
 
4.7%
G4939
 
4.6%
Á4462
 
4.2%
Other values (20)23747
22.4%
Other Punctuation
ValueCountFrequency (%)
.7220
100.0%
Space Separator
ValueCountFrequency (%)
5611
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin106227
89.2%
Common12831
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A16414
15.5%
C11291
10.6%
O10927
10.3%
L9306
 
8.8%
I8570
 
8.1%
D5830
 
5.5%
T5728
 
5.4%
B5013
 
4.7%
G4939
 
4.6%
Á4462
 
4.2%
Other values (20)23747
22.4%
Common
ValueCountFrequency (%)
.7220
56.3%
5611
43.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII113339
95.2%
None5719
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A16414
14.5%
C11291
10.0%
O10927
 
9.6%
L9306
 
8.2%
I8570
 
7.6%
.7220
 
6.4%
D5830
 
5.1%
T5728
 
5.1%
5611
 
5.0%
B5013
 
4.4%
Other values (15)27429
24.2%
None
ValueCountFrequency (%)
Á4462
78.0%
Í1029
 
18.0%
Ú110
 
1.9%
É59
 
1.0%
Ó35
 
0.6%
Ñ15
 
0.3%
Ü9
 
0.2%

cole_cod_depto_ubicacion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.36689342
Minimum5
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:17.990516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q111
median52
Q376
95-th percentile76
Maximum86
Range81
Interquartile range (IQR)65

Descriptive statistics

Standard deviation30.39155652
Coefficient of variation (CV)0.6850052859
Kurtosis-1.864627898
Mean44.36689342
Median Absolute Deviation (MAD)24
Skewness-0.04894403935
Sum684803
Variance923.6467074
MonotonicityNot monotonic
2021-12-11T12:33:18.093411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
766539
42.4%
113610
23.4%
251061
 
6.9%
19739
 
4.8%
5715
 
4.6%
8564
 
3.7%
52486
 
3.1%
66217
 
1.4%
13198
 
1.3%
68196
 
1.3%
Other values (15)1110
 
7.2%
ValueCountFrequency (%)
5715
 
4.6%
8564
 
3.7%
113610
23.4%
13198
 
1.3%
1594
 
0.6%
17158
 
1.0%
1822
 
0.1%
19739
 
4.8%
20168
 
1.1%
2343
 
0.3%
ValueCountFrequency (%)
8644
 
0.3%
858
 
0.1%
8111
 
0.1%
766539
42.4%
7376
 
0.5%
68196
 
1.3%
66217
 
1.4%
6338
 
0.2%
54132
 
0.9%
52486
 
3.1%

cole_depto_ubicacion
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
VALLE
6539 
BOGOTÁ
3610 
CUNDINAMARCA
1061 
CAUCA
739 
ANTIOQUIA
715 
Other values (20)
2771 

Length

Max length15
Median length6
Mean length6.367023
Min length4

Characters and Unicode

Total characters98275
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCESAR
2nd rowNARIÑO
3rd rowCAUCA
4th rowPUTUMAYO
5th rowRISARALDA

Common Values

ValueCountFrequency (%)
VALLE6539
42.4%
BOGOTÁ3610
23.4%
CUNDINAMARCA1061
 
6.9%
CAUCA739
 
4.8%
ANTIOQUIA715
 
4.6%
ATLANTICO564
 
3.7%
NARIÑO486
 
3.1%
RISARALDA217
 
1.4%
BOLIVAR198
 
1.3%
SANTANDER196
 
1.3%
Other values (15)1110
 
7.2%

Length

2021-12-11T12:33:18.217852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valle6539
41.9%
bogotá3610
23.1%
cundinamarca1061
 
6.8%
cauca739
 
4.7%
antioquia715
 
4.6%
atlantico564
 
3.6%
nariño486
 
3.1%
santander328
 
2.1%
risaralda217
 
1.4%
bolivar198
 
1.3%
Other values (16)1151
 
7.4%

Most occurring characters

ValueCountFrequency (%)
A17241
17.5%
L14478
14.7%
O9653
9.8%
E7405
 
7.5%
V6737
 
6.9%
T6184
 
6.3%
N4800
 
4.9%
C4668
 
4.7%
I4216
 
4.3%
B3945
 
4.0%
Other values (14)18948
19.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter98102
99.8%
Space Separator173
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A17241
17.6%
L14478
14.8%
O9653
9.8%
E7405
 
7.5%
V6737
 
6.9%
T6184
 
6.3%
N4800
 
4.9%
C4668
 
4.8%
I4216
 
4.3%
B3945
 
4.0%
Other values (13)18775
19.1%
Space Separator
ValueCountFrequency (%)
173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin98102
99.8%
Common173
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A17241
17.6%
L14478
14.8%
O9653
9.8%
E7405
 
7.5%
V6737
 
6.9%
T6184
 
6.3%
N4800
 
4.9%
C4668
 
4.8%
I4216
 
4.3%
B3945
 
4.0%
Other values (13)18775
19.1%
Common
ValueCountFrequency (%)
173
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII94179
95.8%
None4096
 
4.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A17241
18.3%
L14478
15.4%
O9653
10.2%
E7405
7.9%
V6737
 
7.2%
T6184
 
6.6%
N4800
 
5.1%
C4668
 
5.0%
I4216
 
4.5%
B3945
 
4.2%
Other values (12)14852
15.8%
None
ValueCountFrequency (%)
Á3610
88.1%
Ñ486
 
11.9%

estu_privado_libertad
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
N
15433 
S
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15435
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N15433
> 99.9%
S2
 
< 0.1%

Length

2021-12-11T12:33:18.325910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:18.390727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
n15433
> 99.9%
s2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N15433
> 99.9%
S2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter15435
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N15433
> 99.9%
S2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin15435
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N15433
> 99.9%
S2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N15433
> 99.9%
S2
 
< 0.1%

estu_cod_mcpio_presentacion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43952.39572
Minimum5001
Maximum94001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:18.473950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile8001
Q111001
median52001
Q376001
95-th percentile76520
Maximum94001
Range89000
Interquartile range (IQR)65000

Descriptive statistics

Standard deviation30814.76829
Coefficient of variation (CV)0.7010941675
Kurtosis-1.880995784
Mean43952.39572
Median Absolute Deviation (MAD)24519
Skewness-0.03442289272
Sum678405228
Variance949549944.7
MonotonicityNot monotonic
2021-12-11T12:33:18.591609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
760015396
35.0%
110014059
26.3%
76520720
 
4.7%
5001695
 
4.5%
19001649
 
4.2%
8001568
 
3.7%
25899426
 
2.8%
52001420
 
2.7%
76111309
 
2.0%
13001199
 
1.3%
Other values (54)1994
 
12.9%
ValueCountFrequency (%)
5001695
 
4.5%
504517
 
0.1%
51541
 
< 0.1%
561532
 
0.2%
8001568
 
3.7%
110014059
26.3%
13001199
 
1.3%
134301
 
< 0.1%
1500128
 
0.2%
154075
 
< 0.1%
ValueCountFrequency (%)
940012
 
< 0.1%
880016
 
< 0.1%
863201
 
< 0.1%
8600143
 
0.3%
8500120
 
0.1%
817941
 
< 0.1%
8100112
 
0.1%
768343
 
< 0.1%
7662221
 
0.1%
76520720
4.7%

estu_mcpio_presentacion
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
CALI
5396 
BOGOTÁ D.C.
4059 
PALMIRA
720 
MEDELLÍN
695 
POPAYÁN
649 
Other values (59)
3916 

Length

Max length22
Median length7
Mean length7.858309038
Min length4

Characters and Unicode

Total characters121293
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowVALLEDUPAR
2nd rowIPIALES
3rd rowPOPAYÁN
4th rowMOCOA
5th rowPEREIRA

Common Values

ValueCountFrequency (%)
CALI5396
35.0%
BOGOTÁ D.C.4059
26.3%
PALMIRA720
 
4.7%
MEDELLÍN695
 
4.5%
POPAYÁN649
 
4.2%
BARRANQUILLA568
 
3.7%
ZIPAQUIRÁ426
 
2.8%
PASTO420
 
2.7%
GUADALAJARA DE BUGA309
 
2.0%
CARTAGENA DE INDIAS199
 
1.3%
Other values (54)1994
 
12.9%

Length

2021-12-11T12:33:18.710193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cali5396
25.9%
d.c4059
19.5%
bogotá4059
19.5%
palmira720
 
3.5%
medellín695
 
3.3%
popayán649
 
3.1%
de587
 
2.8%
barranquilla568
 
2.7%
zipaquirá426
 
2.0%
pasto420
 
2.0%
Other values (64)3237
15.6%

Most occurring characters

ValueCountFrequency (%)
A16473
13.6%
C10658
 
8.8%
O10013
 
8.3%
L9900
 
8.2%
I9342
 
7.7%
.8118
 
6.7%
D6192
 
5.1%
T5432
 
4.5%
G5421
 
4.5%
5381
 
4.4%
Other values (21)34363
28.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter107794
88.9%
Other Punctuation8118
 
6.7%
Space Separator5381
 
4.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A16473
15.3%
C10658
9.9%
O10013
9.3%
L9900
9.2%
I9342
 
8.7%
D6192
 
5.7%
T5432
 
5.0%
G5421
 
5.0%
Á5252
 
4.9%
B5210
 
4.8%
Other values (19)23901
22.2%
Other Punctuation
ValueCountFrequency (%)
.8118
100.0%
Space Separator
ValueCountFrequency (%)
5381
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin107794
88.9%
Common13499
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A16473
15.3%
C10658
9.9%
O10013
9.3%
L9900
9.2%
I9342
 
8.7%
D6192
 
5.7%
T5432
 
5.0%
G5421
 
5.0%
Á5252
 
4.9%
B5210
 
4.8%
Other values (19)23901
22.2%
Common
ValueCountFrequency (%)
.8118
60.1%
5381
39.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII115050
94.9%
None6243
 
5.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A16473
14.3%
C10658
 
9.3%
O10013
 
8.7%
L9900
 
8.6%
I9342
 
8.1%
.8118
 
7.1%
D6192
 
5.4%
T5432
 
4.7%
G5421
 
4.7%
5381
 
4.7%
Other values (15)28120
24.4%
None
ValueCountFrequency (%)
Á5252
84.1%
Í784
 
12.6%
Ú112
 
1.8%
É62
 
1.0%
Ó17
 
0.3%
Ñ16
 
0.3%

estu_depto_presentacion
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
VALLE
6535 
BOGOTÁ
4059 
ANTIOQUIA
745 
CAUCA
744 
ATLANTICO
 
568
Other values (23)
2784 

Length

Max length15
Median length6
Mean length6.174991902
Min length4

Characters and Unicode

Total characters95311
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCESAR
2nd rowNARIÑO
3rd rowCAUCA
4th rowPUTUMAYO
5th rowRISARALDA

Common Values

ValueCountFrequency (%)
VALLE6535
42.3%
BOGOTÁ4059
26.3%
ANTIOQUIA745
 
4.8%
CAUCA744
 
4.8%
ATLANTICO568
 
3.7%
CUNDINAMARCA552
 
3.6%
NARIÑO495
 
3.2%
BOLIVAR200
 
1.3%
RISARALDA196
 
1.3%
SANTANDER185
 
1.2%
Other values (18)1156
 
7.5%

Length

2021-12-11T12:33:18.823119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valle6535
41.9%
bogotá4059
26.0%
antioquia745
 
4.8%
cauca744
 
4.8%
atlantico568
 
3.6%
cundinamarca552
 
3.5%
nariño495
 
3.2%
santander320
 
2.0%
bolivar200
 
1.3%
risaralda196
 
1.3%
Other values (20)1200
 
7.7%

Most occurring characters

ValueCountFrequency (%)
A15811
16.6%
L14454
15.2%
O10602
11.1%
E7423
7.8%
V6735
 
7.1%
T6656
 
7.0%
B4404
 
4.6%
G4179
 
4.4%
Á4059
 
4.3%
N3845
 
4.0%
Other values (14)17143
18.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter95132
99.8%
Space Separator179
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A15811
16.6%
L14454
15.2%
O10602
11.1%
E7423
7.8%
V6735
 
7.1%
T6656
 
7.0%
B4404
 
4.6%
G4179
 
4.4%
Á4059
 
4.3%
N3845
 
4.0%
Other values (13)16964
17.8%
Space Separator
ValueCountFrequency (%)
179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin95132
99.8%
Common179
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A15811
16.6%
L14454
15.2%
O10602
11.1%
E7423
7.8%
V6735
 
7.1%
T6656
 
7.0%
B4404
 
4.6%
G4179
 
4.4%
Á4059
 
4.3%
N3845
 
4.0%
Other values (13)16964
17.8%
Common
ValueCountFrequency (%)
179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII90757
95.2%
None4554
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A15811
17.4%
L14454
15.9%
O10602
11.7%
E7423
8.2%
V6735
7.4%
T6656
7.3%
B4404
 
4.9%
G4179
 
4.6%
N3845
 
4.2%
I3762
 
4.1%
Other values (12)12886
14.2%
None
ValueCountFrequency (%)
Á4059
89.1%
Ñ495
 
10.9%

estu_cod_depto_presentacion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.88014253
Minimum5
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:18.929652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q111
median52
Q376
95-th percentile76
Maximum94
Range89
Interquartile range (IQR)65

Descriptive statistics

Standard deviation30.80816997
Coefficient of variation (CV)0.7020982201
Kurtosis-1.88287182
Mean43.88014253
Median Absolute Deviation (MAD)24
Skewness-0.03239557235
Sum677290
Variance949.143337
MonotonicityNot monotonic
2021-12-11T12:33:19.029068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
766535
42.3%
114059
26.3%
5745
 
4.8%
19744
 
4.8%
8568
 
3.7%
25552
 
3.6%
52495
 
3.2%
13200
 
1.3%
66196
 
1.3%
68185
 
1.2%
Other values (18)1156
 
7.5%
ValueCountFrequency (%)
5745
 
4.8%
8568
 
3.7%
114059
26.3%
13200
 
1.3%
1598
 
0.6%
17171
 
1.1%
1829
 
0.2%
19744
 
4.8%
20169
 
1.1%
2347
 
0.3%
ValueCountFrequency (%)
942
 
< 0.1%
886
 
< 0.1%
8644
 
0.3%
8520
 
0.1%
8113
 
0.1%
766535
42.3%
7361
 
0.4%
702
 
< 0.1%
68185
 
1.2%
66196
 
1.3%

punt_lectura_critica
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.30249433
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:19.143186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q152
median61
Q368
95-th percentile76
Maximum100
Range100
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.60175264
Coefficient of variation (CV)0.1956368408
Kurtosis0.1221324502
Mean59.30249433
Median Absolute Deviation (MAD)8
Skewness-0.3014043696
Sum915334
Variance134.6006644
MonotonicityNot monotonic
2021-12-11T12:33:19.269405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68544
 
3.5%
61540
 
3.5%
66539
 
3.5%
63531
 
3.4%
64531
 
3.4%
65528
 
3.4%
59516
 
3.3%
62506
 
3.3%
67503
 
3.3%
60490
 
3.2%
Other values (54)10207
66.1%
ValueCountFrequency (%)
04
 
< 0.1%
223
 
< 0.1%
231
 
< 0.1%
242
 
< 0.1%
255
 
< 0.1%
269
 
0.1%
2716
0.1%
2815
0.1%
2917
0.1%
3033
0.2%
ValueCountFrequency (%)
10059
 
0.4%
833
 
< 0.1%
8212
 
0.1%
8161
 
0.4%
8075
 
0.5%
79116
0.8%
78145
0.9%
77193
1.3%
76219
1.4%
75243
1.6%

percentil_lectura_critica
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct100
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.27366375
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:19.391829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.93168465
Coefficient of variation (CV)0.5754839113
Kurtosis-1.203875362
Mean50.27366375
Median Absolute Deviation (MAD)25
Skewness0.007224072093
Sum775974
Variance837.0423769
MonotonicityNot monotonic
2021-12-11T12:33:19.525276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3163
 
1.1%
1161
 
1.0%
10159
 
1.0%
4159
 
1.0%
7159
 
1.0%
9159
 
1.0%
6158
 
1.0%
24158
 
1.0%
2158
 
1.0%
30157
 
1.0%
Other values (90)13844
89.7%
ValueCountFrequency (%)
1161
1.0%
2158
1.0%
3163
1.1%
4159
1.0%
5157
1.0%
6158
1.0%
7159
1.0%
8155
1.0%
9159
1.0%
10159
1.0%
ValueCountFrequency (%)
100152
1.0%
99155
1.0%
98153
1.0%
97153
1.0%
96153
1.0%
95153
1.0%
94154
1.0%
93153
1.0%
92153
1.0%
91153
1.0%

desemp_lectura_critica
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
3
6853 
4
5090 
2
3054 
1
 
438

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15435
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
36853
44.4%
45090
33.0%
23054
19.8%
1438
 
2.8%

Length

2021-12-11T12:33:19.645652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:19.711865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
36853
44.4%
45090
33.0%
23054
19.8%
1438
 
2.8%

Most occurring characters

ValueCountFrequency (%)
36853
44.4%
45090
33.0%
23054
19.8%
1438
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15435
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
36853
44.4%
45090
33.0%
23054
19.8%
1438
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common15435
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
36853
44.4%
45090
33.0%
23054
19.8%
1438
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII15435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36853
44.4%
45090
33.0%
23054
19.8%
1438
 
2.8%

punt_matematicas
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct72
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.62274052
Minimum0
Maximum100
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:19.800224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q150
median60
Q368
95-th percentile78
Maximum100
Range100
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.52450207
Coefficient of variation (CV)0.2307040228
Kurtosis0.1328777824
Mean58.62274052
Median Absolute Deviation (MAD)9
Skewness-0.2361222519
Sum904842
Variance182.9121562
MonotonicityNot monotonic
2021-12-11T12:33:20.055249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66511
 
3.3%
68483
 
3.1%
64479
 
3.1%
69469
 
3.0%
65465
 
3.0%
67460
 
3.0%
63445
 
2.9%
57431
 
2.8%
60430
 
2.8%
71429
 
2.8%
Other values (62)10833
70.2%
ValueCountFrequency (%)
03
 
< 0.1%
151
 
< 0.1%
162
 
< 0.1%
175
 
< 0.1%
181
 
< 0.1%
195
 
< 0.1%
209
0.1%
2113
0.1%
2213
0.1%
2320
0.1%
ValueCountFrequency (%)
100146
0.9%
8414
 
0.1%
8342
 
0.3%
8253
 
0.3%
8185
 
0.6%
80122
0.8%
79143
0.9%
78179
1.2%
77183
1.2%
76246
1.6%

percentil_matematicas
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct100
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.25895692
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:20.180284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.93834176
Coefficient of variation (CV)0.5757847663
Kurtosis-1.204921369
Mean50.25895692
Median Absolute Deviation (MAD)25
Skewness0.007535512243
Sum775747
Variance837.427624
MonotonicityNot monotonic
2021-12-11T12:33:20.306503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2160
 
1.0%
3160
 
1.0%
4159
 
1.0%
1159
 
1.0%
5159
 
1.0%
7159
 
1.0%
6158
 
1.0%
11158
 
1.0%
10158
 
1.0%
19157
 
1.0%
Other values (90)13848
89.7%
ValueCountFrequency (%)
1159
1.0%
2160
1.0%
3160
1.0%
4159
1.0%
5159
1.0%
6158
1.0%
7159
1.0%
8156
1.0%
9157
1.0%
10158
1.0%
ValueCountFrequency (%)
100151
1.0%
99155
1.0%
98152
1.0%
97154
1.0%
96153
1.0%
95154
1.0%
94153
1.0%
93151
1.0%
92155
1.0%
91155
1.0%

desemp_matematicas
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
3
8453 
2
3149 
4
2925 
1
908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15435
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
38453
54.8%
23149
 
20.4%
42925
 
19.0%
1908
 
5.9%

Length

2021-12-11T12:33:20.421206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:20.489488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
38453
54.8%
23149
 
20.4%
42925
 
19.0%
1908
 
5.9%

Most occurring characters

ValueCountFrequency (%)
38453
54.8%
23149
 
20.4%
42925
 
19.0%
1908
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15435
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
38453
54.8%
23149
 
20.4%
42925
 
19.0%
1908
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common15435
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
38453
54.8%
23149
 
20.4%
42925
 
19.0%
1908
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII15435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38453
54.8%
23149
 
20.4%
42925
 
19.0%
1908
 
5.9%

punt_c_naturales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.06524133
Minimum0
Maximum100
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:20.577362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q147
median57
Q365
95-th percentile74
Maximum100
Range100
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.37377713
Coefficient of variation (CV)0.2207031814
Kurtosis-0.3503738956
Mean56.06524133
Median Absolute Deviation (MAD)9
Skewness-0.1842151339
Sum865367
Variance153.1103604
MonotonicityNot monotonic
2021-12-11T12:33:20.705687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63492
 
3.2%
61488
 
3.2%
58480
 
3.1%
65478
 
3.1%
60470
 
3.0%
59468
 
3.0%
57461
 
3.0%
55454
 
2.9%
64443
 
2.9%
66433
 
2.8%
Other values (54)10768
69.8%
ValueCountFrequency (%)
03
 
< 0.1%
211
 
< 0.1%
223
 
< 0.1%
238
 
0.1%
249
 
0.1%
2511
 
0.1%
2622
 
0.1%
2735
0.2%
2831
0.2%
2955
0.4%
ValueCountFrequency (%)
10039
 
0.3%
826
 
< 0.1%
8136
 
0.2%
8060
 
0.4%
7969
 
0.4%
7882
 
0.5%
77123
0.8%
76139
0.9%
75182
1.2%
74223
1.4%

percentil_c_naturales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct100
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.30223518
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:20.829516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.88912896
Coefficient of variation (CV)0.5743110393
Kurtosis-1.201499441
Mean50.30223518
Median Absolute Deviation (MAD)25
Skewness0.008574975208
Sum776415
Variance834.5817722
MonotonicityNot monotonic
2021-12-11T12:33:20.954649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5159
 
1.0%
7159
 
1.0%
10159
 
1.0%
22158
 
1.0%
3158
 
1.0%
24157
 
1.0%
25157
 
1.0%
1157
 
1.0%
42157
 
1.0%
2157
 
1.0%
Other values (90)13857
89.8%
ValueCountFrequency (%)
1157
1.0%
2157
1.0%
3158
1.0%
4153
1.0%
5159
1.0%
6156
1.0%
7159
1.0%
8153
1.0%
9156
1.0%
10159
1.0%
ValueCountFrequency (%)
100152
1.0%
99154
1.0%
98153
1.0%
97152
1.0%
96153
1.0%
95153
1.0%
94154
1.0%
93153
1.0%
92155
1.0%
91154
1.0%

desemp_c_naturales
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
3
6590 
2
5034 
1
1956 
4
1855 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15435
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
36590
42.7%
25034
32.6%
11956
 
12.7%
41855
 
12.0%

Length

2021-12-11T12:33:21.071292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:21.139364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
36590
42.7%
25034
32.6%
11956
 
12.7%
41855
 
12.0%

Most occurring characters

ValueCountFrequency (%)
36590
42.7%
25034
32.6%
11956
 
12.7%
41855
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15435
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
36590
42.7%
25034
32.6%
11956
 
12.7%
41855
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Common15435
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
36590
42.7%
25034
32.6%
11956
 
12.7%
41855
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36590
42.7%
25034
32.6%
11956
 
12.7%
41855
 
12.0%

punt_sociales_ciudadanas
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.22299968
Minimum0
Maximum100
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:21.226230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q145
median57
Q366
95-th percentile76
Maximum100
Range100
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.79299194
Coefficient of variation (CV)0.2497689734
Kurtosis-0.5917944183
Mean55.22299968
Median Absolute Deviation (MAD)10
Skewness-0.2151740598
Sum852367
Variance190.2466266
MonotonicityNot monotonic
2021-12-11T12:33:21.352328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61431
 
2.8%
58422
 
2.7%
63422
 
2.7%
60418
 
2.7%
59410
 
2.7%
62397
 
2.6%
65394
 
2.6%
68392
 
2.5%
64387
 
2.5%
66387
 
2.5%
Other values (58)11375
73.7%
ValueCountFrequency (%)
02
 
< 0.1%
184
 
< 0.1%
195
 
< 0.1%
205
 
< 0.1%
2120
 
0.1%
2217
 
0.1%
2331
0.2%
2439
0.3%
2549
0.3%
2661
0.4%
ValueCountFrequency (%)
10030
 
0.2%
8325
 
0.2%
8237
 
0.2%
8160
 
0.4%
8078
 
0.5%
7993
0.6%
78122
0.8%
77163
1.1%
76176
1.1%
75220
1.4%

percentil_sociales_ciudadanas
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct100
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.29290573
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:21.477253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.90306864
Coefficient of variation (CV)0.5746947451
Kurtosis-1.201760108
Mean50.29290573
Median Absolute Deviation (MAD)25
Skewness0.00744225576
Sum776271
Variance835.3873768
MonotonicityNot monotonic
2021-12-11T12:33:21.602748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1164
 
1.1%
2160
 
1.0%
5160
 
1.0%
25160
 
1.0%
32158
 
1.0%
15157
 
1.0%
40157
 
1.0%
6157
 
1.0%
21157
 
1.0%
14156
 
1.0%
Other values (90)13849
89.7%
ValueCountFrequency (%)
1164
1.1%
2160
1.0%
3155
1.0%
4155
1.0%
5160
1.0%
6157
1.0%
7155
1.0%
8153
1.0%
9156
1.0%
10155
1.0%
ValueCountFrequency (%)
100153
1.0%
99153
1.0%
98153
1.0%
97153
1.0%
96153
1.0%
95154
1.0%
94153
1.0%
93152
1.0%
92153
1.0%
91153
1.0%

desemp_sociales_ciudadanas
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
3
5888 
2
4709 
1
2636 
4
2202 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15435
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
35888
38.1%
24709
30.5%
12636
17.1%
42202
 
14.3%

Length

2021-12-11T12:33:21.718200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:21.783744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
35888
38.1%
24709
30.5%
12636
17.1%
42202
 
14.3%

Most occurring characters

ValueCountFrequency (%)
35888
38.1%
24709
30.5%
12636
17.1%
42202
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15435
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
35888
38.1%
24709
30.5%
12636
17.1%
42202
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common15435
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
35888
38.1%
24709
30.5%
12636
17.1%
42202
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35888
38.1%
24709
30.5%
12636
17.1%
42202
 
14.3%

punt_ingles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct69
Distinct (%)0.4%
Missing36
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean64.94863303
Minimum0
Maximum100
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:22.004726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q152
median67
Q378
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.86333866
Coefficient of variation (CV)0.2750379465
Kurtosis-0.502889619
Mean64.94863303
Median Absolute Deviation (MAD)13
Skewness-0.1994844491
Sum1000144
Variance319.098868
MonotonicityNot monotonic
2021-12-11T12:33:22.124114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100854
 
5.5%
79467
 
3.0%
78461
 
3.0%
76446
 
2.9%
77442
 
2.9%
85432
 
2.8%
81424
 
2.7%
75418
 
2.7%
80408
 
2.6%
82401
 
2.6%
Other values (59)10646
69.0%
ValueCountFrequency (%)
014
 
0.1%
213
 
< 0.1%
226
 
< 0.1%
2318
 
0.1%
2424
 
0.2%
2530
0.2%
2640
0.3%
2744
0.3%
2852
0.3%
2974
0.5%
ValueCountFrequency (%)
100854
5.5%
8745
 
0.3%
86167
 
1.1%
85432
2.8%
84238
 
1.5%
83355
2.3%
82401
2.6%
81424
2.7%
80408
2.6%
79467
3.0%

percentil_ingles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct95
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.42759961
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:22.248381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation29.17741178
Coefficient of variation (CV)0.5786000525
Kurtosis-1.172510904
Mean50.42759961
Median Absolute Deviation (MAD)25
Skewness0.02842087687
Sum778350
Variance851.3213582
MonotonicityNot monotonic
2021-12-11T12:33:22.371875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100906
 
5.9%
1191
 
1.2%
92188
 
1.2%
94169
 
1.1%
90163
 
1.1%
8159
 
1.0%
45157
 
1.0%
2156
 
1.0%
10156
 
1.0%
17156
 
1.0%
Other values (85)13034
84.4%
ValueCountFrequency (%)
1191
1.2%
2156
1.0%
3155
1.0%
4155
1.0%
5154
1.0%
6154
1.0%
7155
1.0%
8159
1.0%
9156
1.0%
10156
1.0%
ValueCountFrequency (%)
100906
5.9%
94169
 
1.1%
93108
 
0.7%
92188
 
1.2%
91156
 
1.0%
90163
 
1.1%
89152
 
1.0%
88155
 
1.0%
87153
 
1.0%
86153
 
1.0%

desemp_ingles
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
B1
3823 
B+
3791 
A-
3052 
A2
2524 
A1
2245 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters30870
Distinct characters6
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-
2nd rowA-
3rd rowA-
4th rowA-
5th rowB+

Common Values

ValueCountFrequency (%)
B13823
24.8%
B+3791
24.6%
A-3052
19.8%
A22524
16.4%
A12245
14.5%

Length

2021-12-11T12:33:22.480546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:22.547716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
b13823
24.8%
b3791
24.6%
a3052
19.8%
a22524
16.4%
a12245
14.5%

Most occurring characters

ValueCountFrequency (%)
A7821
25.3%
B7614
24.7%
16068
19.7%
+3791
12.3%
-3052
 
9.9%
22524
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter15435
50.0%
Decimal Number8592
27.8%
Math Symbol3791
 
12.3%
Dash Punctuation3052
 
9.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A7821
50.7%
B7614
49.3%
Decimal Number
ValueCountFrequency (%)
16068
70.6%
22524
29.4%
Math Symbol
ValueCountFrequency (%)
+3791
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15435
50.0%
Common15435
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
16068
39.3%
+3791
24.6%
-3052
19.8%
22524
16.4%
Latin
ValueCountFrequency (%)
A7821
50.7%
B7614
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A7821
25.3%
B7614
24.7%
16068
19.7%
+3791
12.3%
-3052
 
9.9%
22524
 
8.2%

punt_global
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct328
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.4343375
Minimum0
Maximum479
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:22.637118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile183
Q1245
median295
Q3336
95-th percentile377.3
Maximum479
Range479
Interquartile range (IQR)91

Descriptive statistics

Standard deviation60.29918507
Coefficient of variation (CV)0.2083345935
Kurtosis-0.5756648312
Mean289.4343375
Median Absolute Deviation (MAD)45
Skewness-0.2563734515
Sum4467419
Variance3635.99172
MonotonicityNot monotonic
2021-12-11T12:33:22.760155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317125
 
0.8%
322122
 
0.8%
338121
 
0.8%
320121
 
0.8%
312118
 
0.8%
325118
 
0.8%
340117
 
0.8%
328116
 
0.8%
333116
 
0.8%
313116
 
0.8%
Other values (318)14245
92.3%
ValueCountFrequency (%)
01
 
< 0.1%
141
 
< 0.1%
691
 
< 0.1%
911
 
< 0.1%
931
 
< 0.1%
1211
 
< 0.1%
1232
 
< 0.1%
1321
 
< 0.1%
1335
< 0.1%
1341
 
< 0.1%
ValueCountFrequency (%)
4791
< 0.1%
4751
< 0.1%
4721
< 0.1%
4711
< 0.1%
4681
< 0.1%
4571
< 0.1%
4551
< 0.1%
4542
< 0.1%
4502
< 0.1%
4492
< 0.1%

percentil_global
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct100
Distinct (%)0.6%
Missing36
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean50.26923826
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.7 KiB
2021-12-11T12:33:22.887320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.88615423
Coefficient of variation (CV)0.5746288432
Kurtosis-1.20221751
Mean50.26923826
Median Absolute Deviation (MAD)25
Skewness0.007336475248
Sum774096
Variance834.4099064
MonotonicityNot monotonic
2021-12-11T12:33:23.015941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64186
 
1.2%
42184
 
1.2%
72184
 
1.2%
23178
 
1.2%
84177
 
1.1%
57177
 
1.1%
25173
 
1.1%
93171
 
1.1%
17170
 
1.1%
46167
 
1.1%
Other values (90)13632
88.3%
ValueCountFrequency (%)
1158
1.0%
2160
1.0%
3159
1.0%
4150
1.0%
5164
1.1%
6150
1.0%
7154
1.0%
8167
1.1%
9153
1.0%
10161
1.0%
ValueCountFrequency (%)
100149
1.0%
99147
1.0%
98153
1.0%
97156
1.0%
96135
0.9%
95165
1.1%
94137
0.9%
93171
1.1%
92158
1.0%
91146
0.9%

estu_estadoinvestigacion
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
PUBLICAR
15219 
VALIDEZ OFICINA JURÍDICA
 
216

Length

Max length24
Median length8
Mean length8.223906706
Min length8

Characters and Unicode

Total characters126936
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVALIDEZ OFICINA JURÍDICA
2nd rowPUBLICAR
3rd rowPUBLICAR
4th rowPUBLICAR
5th rowPUBLICAR

Common Values

ValueCountFrequency (%)
PUBLICAR15219
98.6%
VALIDEZ OFICINA JURÍDICA216
 
1.4%

Length

2021-12-11T12:33:23.143862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:23.214492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
publicar15219
95.9%
validez216
 
1.4%
oficina216
 
1.4%
jurídica216
 
1.4%

Most occurring characters

ValueCountFrequency (%)
I16083
12.7%
A15867
12.5%
C15651
12.3%
U15435
12.2%
L15435
12.2%
R15435
12.2%
P15219
12.0%
B15219
12.0%
432
 
0.3%
D432
 
0.3%
Other values (8)1728
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter126504
99.7%
Space Separator432
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I16083
12.7%
A15867
12.5%
C15651
12.4%
U15435
12.2%
L15435
12.2%
R15435
12.2%
P15219
12.0%
B15219
12.0%
D432
 
0.3%
E216
 
0.2%
Other values (7)1512
 
1.2%
Space Separator
ValueCountFrequency (%)
432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin126504
99.7%
Common432
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I16083
12.7%
A15867
12.5%
C15651
12.4%
U15435
12.2%
L15435
12.2%
R15435
12.2%
P15219
12.0%
B15219
12.0%
D432
 
0.3%
E216
 
0.2%
Other values (7)1512
 
1.2%
Common
ValueCountFrequency (%)
432
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII126720
99.8%
None216
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I16083
12.7%
A15867
12.5%
C15651
12.4%
U15435
12.2%
L15435
12.2%
R15435
12.2%
P15219
12.0%
B15219
12.0%
432
 
0.3%
D432
 
0.3%
Other values (7)1512
 
1.2%
None
ValueCountFrequency (%)
Í216
100.0%

estu_generacion_e
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.7 KiB
NO
13389 
GENERACION E - GRATUIDAD
1927 
GENERACION E - EXCELENCIA NACIONAL
 
117
GENERACION E - EXCELENCIA DEPARTAMENTAL
 
2

Length

Max length39
Median length2
Mean length4.993974733
Min length2

Characters and Unicode

Total characters77082
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGENERACION E - GRATUIDAD
2nd rowGENERACION E - GRATUIDAD
3rd rowGENERACION E - GRATUIDAD
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO13389
86.7%
GENERACION E - GRATUIDAD1927
 
12.5%
GENERACION E - EXCELENCIA NACIONAL117
 
0.8%
GENERACION E - EXCELENCIA DEPARTAMENTAL2
 
< 0.1%

Length

2021-12-11T12:33:23.293751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-11T12:33:23.367140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no13389
61.7%
generacion2046
 
9.4%
e2046
 
9.4%
2046
 
9.4%
gratuidad1927
 
8.9%
excelencia119
 
0.5%
nacional117
 
0.5%
departamental2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N17836
23.1%
O15552
20.2%
E6499
 
8.4%
A6259
 
8.1%
6257
 
8.1%
I4209
 
5.5%
R3975
 
5.2%
G3973
 
5.2%
D3856
 
5.0%
C2401
 
3.1%
Other values (7)6265
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter68779
89.2%
Space Separator6257
 
8.1%
Dash Punctuation2046
 
2.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N17836
25.9%
O15552
22.6%
E6499
 
9.4%
A6259
 
9.1%
I4209
 
6.1%
R3975
 
5.8%
G3973
 
5.8%
D3856
 
5.6%
C2401
 
3.5%
T1931
 
2.8%
Other values (5)2288
 
3.3%
Space Separator
ValueCountFrequency (%)
6257
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin68779
89.2%
Common8303
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N17836
25.9%
O15552
22.6%
E6499
 
9.4%
A6259
 
9.1%
I4209
 
6.1%
R3975
 
5.8%
G3973
 
5.8%
D3856
 
5.6%
C2401
 
3.5%
T1931
 
2.8%
Other values (5)2288
 
3.3%
Common
ValueCountFrequency (%)
6257
75.4%
-2046
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII77082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N17836
23.1%
O15552
20.2%
E6499
 
8.4%
A6259
 
8.1%
6257
 
8.1%
I4209
 
5.5%
R3975
 
5.2%
G3973
 
5.2%
D3856
 
5.0%
C2401
 
3.1%
Other values (7)6265
 
8.1%

Interactions

2021-12-11T12:33:01.975366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:11.799208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:14.355698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:16.850148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:19.410702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:21.948500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:24.479576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:27.203831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:29.892155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:32.356379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:34.642413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:37.135809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:39.652042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:42.129880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:44.638506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:47.138413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:49.653766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:52.038336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:54.551602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:56.999672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:59.495695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:02.078135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:11.935723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:14.456424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:16.962290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:19.636858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:22.061439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:24.601259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:27.316540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:29.998618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:32.462932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:34.860405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:37.251810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:39.754389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:42.234188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:44.750230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:47.243758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:49.762559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:52.149996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:54.658568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:57.105932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:59.601786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:02.185004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:12.042344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:14.559863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:17.078372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:19.744500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:22.168139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:24.714799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:27.427882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:30.100636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:32.566102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:34.964945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:37.355934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:39.860110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:42.344838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:44.853870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:47.350497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:49.868404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:52.256997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:54.761533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:57.211953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:59.705023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:02.307884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:12.168147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:14.679608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:17.207267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:19.867711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:22.290993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:24.850601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:27.557210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:30.216361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:32.680530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:35.087319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:37.478042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:39.980788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:42.471879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:44.981658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:47.471946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:49.990040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:52.390449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:54.879063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:57.331352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:59.823858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:02.424014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:12.282954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:14.793291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:17.330360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:19.987741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:22.406217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:24.980095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:27.675729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:30.327836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:32.793825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:35.200430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:37.592623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:40.095421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:42.589313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:45.105852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:47.588410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:50.109186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:52.623733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:54.999070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:57.451967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:59.940301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:02.536475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:12.401036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:14.907725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:17.453030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:20.108251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:22.648231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:25.103183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:27.799767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:30.446275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:32.900972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:35.312688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:37.832966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:40.208573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:42.704835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:45.215430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:47.702707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:50.220340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:52.735580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:55.109711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:57.571768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:00.063617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:02.657614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:12.537065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:15.032675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:17.586736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:20.233494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:22.773150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:25.238309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:27.941535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:30.570208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:33.026015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:35.439866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:37.955920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:40.333251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:42.829174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:45.340274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:47.828349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:50.344212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:52.868067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:55.230475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:57.696596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:00.192235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-11T12:32:15.158209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-11T12:32:25.369868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-11T12:32:38.076573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-11T12:32:50.465359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:52.989738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:55.349340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:57.819734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:00.320992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:02.891414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:12.761126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:15.269013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:17.833447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:20.468203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:23.002870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:25.491485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:28.187970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:30.795316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:33.242577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:35.677526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-11T12:32:16.406923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:19.054589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:21.606244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:24.137435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:26.834044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:29.527339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:32.025603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:34.309259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:36.796664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:39.319011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:41.789303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:44.302600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:46.804342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:49.187415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:51.701929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:54.214692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:56.671665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:59.167930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:01.649624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:04.133243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:14.133941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:16.633131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:19.175347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:21.719424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:24.245579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:26.954672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:29.645022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:32.140653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:34.421229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:36.905190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:39.428996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:41.905671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:44.420226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:46.913522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:49.305979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:51.815570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:54.327176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:56.777213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:59.275650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:01.756529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:04.359231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:14.247344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:16.737456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:19.295654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:21.836347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:24.355381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:27.082758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:29.770478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:32.245273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:34.532236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:37.018883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:39.536732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:42.018589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:44.530651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:47.028069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:49.424179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:51.925268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:54.441859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:56.884924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:32:59.386913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-11T12:33:01.865347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-12-11T12:33:23.493008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-11T12:33:23.990336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-11T12:33:24.366366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-11T12:33:24.774785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-11T12:33:26.130626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-11T12:33:05.065417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-11T12:33:07.181924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-11T12:33:08.306175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

estu_tipodocumentoestu_nacionalidadestu_generoestu_fechanacimientoperiodoestu_consecutivoestu_estudianteestu_pais_resideestu_tieneetniaestu_depto_resideestu_cod_reside_deptoestu_mcpio_resideestu_cod_reside_mcpiofami_estratoviviendafami_personashogarfami_cuartoshogarfami_educacionpadrefami_educacionmadrefami_trabajolaborpadrefami_trabajolabormadrefami_tieneinternetfami_tieneserviciotvfami_tienecomputadorfami_tienelavadorafami_tienehornomicroogasfami_tieneautomovilfami_tienemotocicletafami_tieneconsolavideojuegosfami_numlibrosfami_comelechederivadosfami_comecarnepescadohuevofami_comecerealfrutoslegumbrefami_situacioneconomicaestu_dedicacionlecturadiariaestu_dedicacioninternetestu_horassemanatrabajaestu_tiporemuneracioncole_codigo_icfescole_cod_dane_establecimientocole_nombre_establecimientocole_generocole_naturalezacole_calendariocole_bilinguecole_caractercole_cod_dane_sedecole_nombre_sedecole_sede_principalcole_area_ubicacioncole_jornadacole_cod_mcpio_ubicacioncole_mcpio_ubicacioncole_cod_depto_ubicacioncole_depto_ubicacionestu_privado_libertadestu_cod_mcpio_presentacionestu_mcpio_presentacionestu_depto_presentacionestu_cod_depto_presentacionpunt_lectura_criticapercentil_lectura_criticadesemp_lectura_criticapunt_matematicaspercentil_matematicasdesemp_matematicaspunt_c_naturalespercentil_c_naturalesdesemp_c_naturalespunt_sociales_ciudadanaspercentil_sociales_ciudadanasdesemp_sociales_ciudadanaspunt_inglespercentil_inglesdesemp_inglespunt_globalpercentil_globalestu_estadoinvestigacionestu_generacion_e
0CCCOLOMBIAF1985-01-01T00:00:00.00020201SB11202010045555ESTUDIANTECOLOMBIANoCESAR20.0SAN DIEGO20750.0Estrato 15 a 6UnoPrimaria incompletaPrimaria incompletaTrabaja en el hogar, no trabaja o estudiaTrabaja por cuenta propia (por ejemplo plomero, electricista)NoNoNoNoNoNoNoNo0 A 10 LIBROS1 o 2 veces por semana1 o 2 veces por semanaTodos o casi todos los díasMejorNo leo por entretenimientoNo Navega InternetMás de 30 horasSi, en efectivo57372120750000415I.E. MANUEL RODRIGUEZ TORICESMIXTOOFICIALASTÉCNICO/ACADÉMICO120750000415I.E. MANUEL RODRIGUEZ TORICESSURBANONOCHE20750SAN DIEGO20CESARN20001VALLEDUPARCESAR20396232413561241136.07A-1642.0VALIDEZ OFICINA JURÍDICAGENERACION E - GRATUIDAD
1CCCOLOMBIAF1995-01-01T00:00:00.00020201SB11202010045719ESTUDIANTECOLOMBIANoNARIÑO52.0IPIALES52356.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN177618352356001951INSTITUTO DE EDUCACIÓN TECNICA INESURMIXTONO OFICIALANaNNaN352356001951INSTITUTO DE EDUCACIÓN TECNICA INESUR - SEDE PRINCIPALSURBANOSABATINA52356IPIALES52NARIÑON52356IPIALESNARIÑO52417241122391014423230.03A-20210.0PUBLICARGENERACION E - GRATUIDAD
2CCCOLOMBIAF1997-01-01T00:00:00.00020201SB11202010070662ESTUDIANTECOLOMBIASiCAUCA19.0TOTORÓ19824.0Estrato 13 a 4CuatroSecundaria (Bachillerato) incompletaSecundaria (Bachillerato) incompletaTrabaja en el hogar, no trabaja o estudiaTrabaja en el hogar, no trabaja o estudiaNoNoNoNoNoNoNoNo0 A 10 LIBROS1 o 2 veces por semanaNaNNaNIgual30 minutos o menosNo Navega InternetEntre 11 y 20 horasSi, en efectivo135301319001004669COORPORACION EDUCATIVA DEL SUR OCCIDENTE COLOMBIANOMIXTONO OFICIALOTRONaNACADÉMICO319001004669COORPORACION EDUCATIVA DEL SUR OCCIDENTE COLOMBIANO - SEDE PRINCIPALSURBANOMAÑANA19001POPAYÁN19CAUCAN19001POPAYÁNCAUCA19374236723341241130.03A-1622.0PUBLICARGENERACION E - GRATUIDAD
3CCCOLOMBIAF2001-01-01T00:00:00.00020201SB11202010069926ESTUDIANTECOLOMBIANoPUTUMAYO86.0MOCOA86001.0Estrato 11 a 2UnoTécnica o tecnológica completaEducación profesional completaEs dueño de un negocio grande, tiene un cargo de nivel directivo o gerencialTrabaja como profesional (por ejemplo médico, abogado, ingeniero)SiNoSiSiNoNoSiNo11 A 25 LIBROS1 o 2 veces por semana3 a 5 veces por semana1 o 2 veces por semanaIgual30 minutos o menosEntre 1 y 3 horasEntre 11 y 20 horasSi, en efectivo155739386001003939NUEVO INSTITUTO DE APRENDIZAJE SURCOLOMBIANOMIXTONO OFICIALANaNTÉCNICO/ACADÉMICO386001003939NUEVO INSTITUTO DE APRENDIZAJE SURCOLOMBIANO - SEDE PRINCIPALSURBANOSABATINA86001MOCOA86PUTUMAYON86001MOCOAPUTUMAYO8638524213238101337137.08A-1886.0PUBLICARNO
4CCCOLOMBIAF2001-02-01T00:00:00.00020201SB11202010023181ESTUDIANTECOLOMBIANoRISARALDA66.0PEREIRA66001.0Estrato 63 a 4CuatroEducación profesional completaSecundaria (Bachillerato) completaTrabaja como profesional (por ejemplo médico, abogado, ingeniero)Trabaja en el hogar, no trabaja o estudiaSiSiSiSiSiSiNoNo26 A 100 LIBROSTodos o casi todos los díasTodos o casi todos los días3 a 5 veces por semanaIgualNo leo por entretenimientoMás de 3 horas0No77776366001003814COL FUNDACION LIC INGLESMIXTONO OFICIALBSACADÉMICO366001003814COL FUNDACION LIC INGLESSRURALCOMPLETA66001PEREIRA66RISARALDAN66001PEREIRARISARALDA665842350252503225342280.080B+27439.0PUBLICARNO
5CCCOLOMBIAF2001-02-01T00:00:00.00020201SB11202010057992ESTUDIANTECOLOMBIANoANTIOQUIA5.0MACEO5425.0Estrato 17 a 8CuatroPrimaria completaPrimaria incompletaTrabaja como personal de limpieza, mantenimiento, seguridad o construcciónTrabaja en el hogar, no trabaja o estudiaSiNoSiSiSiNoNoNo0 A 10 LIBROS1 o 2 veces por semana3 a 5 veces por semana1 o 2 veces por semanaPeorEntre 30 y 60 minutosMás de 3 horas0No662858305001022461COL GENTE UNIDA JOVENES POR LA PAZ - LUZ DE ORIENTEMIXTONO OFICIALANaNTÉCNICO/ACADÉMICO305001022461COL GENTE UNIDA JOVENES POR LA PAZ - LUZ DE ORIENTESURBANOSABATINA5001MEDELLÍN5ANTIOQUIAN5001MEDELLÍNANTIOQUIA5481824416244202262129.02A-1989.0PUBLICARGENERACION E - GRATUIDAD
6CCCOLOMBIAF2001-03-01T00:00:00.00020201SB11202010074718ESTUDIANTECOLOMBIANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN177618352356001951INSTITUTO DE EDUCACIÓN TECNICA INESURMIXTONO OFICIALANaNNaN352356001951INSTITUTO DE EDUCACIÓN TECNICA INESUR - SEDE PRINCIPALSURBANOSABATINA52356IPIALES52NARIÑON52356IPIALESNARIÑO523221391022711293136.07A-1602.0PUBLICARGENERACION E - GRATUIDAD
7CCCOLOMBIAF1982-04-01T00:00:00.00020201SB11202010070513ESTUDIANTECOLOMBIANoANTIOQUIA5.0MEDELLÍN5001.0Estrato 13 a 4DosNingunoPrimaria incompletaTrabaja en el hogar, no trabaja o estudiaNaNNoNoNoNoNoNoNoNo0 A 10 LIBROS1 o 2 veces por semanaNunca o rara vez comemos esoNunca o rara vez comemos esoIgual30 minutos o menosNo Navega InternetMás de 30 horasSi, en efectivo95869305001022682POLITÉCNICO MAYOR AGENCIA CRISTIANA DE SERVICIO Y EDUCACIÓNMIXTONO OFICIALOTRONACADÉMICO305001022682POLITÉCNICO MAYOR AGENCIA CRISTIANA DE SERVICIO Y EDUCACIÓNSURBANOMAÑANA5001MEDELLÍN5ANTIOQUIAN5001MEDELLÍNANTIOQUIA5451323672442024118239.010A-20711.0PUBLICARGENERACION E - GRATUIDAD
8CCCOLOMBIAF1989-04-01T00:00:00.00020201SB11202010067334ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 55 a 6TresNingunoPrimaria incompletaTrabaja en el hogar, no trabaja o estudiaTrabaja en el hogar, no trabaja o estudiaSiNoSiSiNoNoNoNo26 A 100 LIBROS3 a 5 veces por semana1 o 2 veces por semanaTodos o casi todos los díasPeorEntre 30 y 60 minutosEntre 30 y 60 minutosMás de 30 horasSi, en efectivo88575311001047723COL CENT DE PROMOCION SAN JOSEMIXTONO OFICIALANACADÉMICO311001047723COL CENT DE PROMOCION SAN JOSESURBANOSABATINA11001BOGOTÁ D.C.11BOGOTÁN11001BOGOTÁ D.C.BOGOTÁ113531292128113915129.02A-1622.0PUBLICARNO
9CCCOLOMBIAF1999-04-01T00:00:00.00020201SB11202010069900ESTUDIANTECOLOMBIANoMETA50.0ACACÍAS50006.0Estrato 25 a 6TresNingunoPrimaria incompletaEs vendedor o trabaja en atención al públicoTrabaja en el hogar, no trabaja o estudiaSiSiSiSiSiNoNoNo0 A 10 LIBROS1 o 2 veces por semana3 a 5 veces por semanaTodos o casi todos los díasIgual30 minutos o menosMás de 3 horasEntre 21 y 30 horasSi, en efectivo127944350001006933GIMNASIO INTERACTIVO KAIZENMIXTONO OFICIALANaNACADÉMICO350001006933GIMNASIO INTERACTIVO KAIZEN - SEDE PRINCIPALSURBANOSABATINA50001VILLAVICENCIO50METAN50001VILLAVICENCIOMETA50532933241421524525233.05A-21113.0PUBLICARNO

Last rows

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15425TIESTADOS UNIDOSM2002-07-24T00:00:00.00020201SB11202010048235ESTUDIANTEESTADOS UNIDOSNoVALLE76.0CALI76001.0Estrato 33 a 4TresNo sabeEducación profesional completaNo sabePensionadoSiSiSiSiSiSiNoSi11 A 25 LIBROS3 a 5 veces por semana1 o 2 veces por semana1 o 2 veces por semanaIgual30 minutos o menosEntre 30 y 60 minutos0No100552376001012789COL TECNICO MARIA ELVINIAMIXTONO OFICIALBNTÉCNICO376001012789COL TECNICO MARIA ELVINIASURBANOMAÑANA76001CALI76VALLEN76001CALIVALLE765534353333513524526256.032A125730.0PUBLICARNO
15426TIESTADOS UNIDOSM0003-09-24T00:00:00.00020201SB11202010003574ESTUDIANTEESTADOS UNIDOSNoVALLE76.0CALI76001.0Estrato 41 a 2DosSecundaria (Bachillerato) completaTécnica o tecnológica incompletaEs dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etcEs vendedor o trabaja en atención al públicoSiSiSiSiSiSiNoNo26 A 100 LIBROS3 a 5 veces por semana3 a 5 veces por semana1 o 2 veces por semanaPeorEntre 30 y 60 minutosEntre 1 y 3 horas0No17004376001002376COLEGIO HISPANOAMERICANOMIXTONO OFICIALBNACADÉMICO376001002376COLEGIO HISPANOAMERICANOSURBANOCOMPLETA76001CALI76VALLEN76001CALIVALLE767695478964769746880377.072B137394.0PUBLICARNO
15427TIESTADOS UNIDOSM2003-04-28T00:00:00.00020201SB11202010003591ESTUDIANTEESTADOS UNIDOSNoANTIOQUIA5.0ENVIGADO5266.0Estrato 55 a 6CincoEducación profesional completaEducación profesional completaEs dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etcTrabaja en el hogar, no trabaja o estudiaSiSiSiSiSiSiNoSi26 A 100 LIBROS3 a 5 veces por semanaTodos o casi todos los díasTodos o casi todos los díasIgualEntre 30 y 60 minutosEntre 1 y 3 horas0No126409305001019681CORPORACION COLEGIO CUMBRESMIXTONO OFICIALBNaNACADÉMICO305001019681CORPORACION COLEGIO CUMBRESSURBANOCOMPLETA5266ENVIGADO5ANTIOQUIAN5001MEDELLÍNANTIOQUIA56874459473718846880383.089B+33977.0PUBLICARNO
15428TIESTADOS UNIDOSM0002-10-28T00:00:00.00020201SB11202010003688ESTUDIANTEESTADOS UNIDOSNoVALLE76.0CALI76001.0Estrato 39 o másCuatroSecundaria (Bachillerato) completaSecundaria (Bachillerato) incompletaTiene un trabajo de tipo auxiliar administrativo (por ejemplo, secretario o asistente)No aplicaSiNoSiSiSiNoSiNoNaNNunca o rara vez comemos eso1 o 2 veces por semanaTodos o casi todos los díasPeor30 minutos o menosMás de 3 horas0No17228376001000314COLEGIO PARROQIAL SANTIAGO APOSTOLMIXTONO OFICIALBNTÉCNICO/ACADÉMICO376001000314COLEGIO PARROQIAL SANTIAGO APOSTOLSURBANOMAÑANA76001CALI76VALLEN76001CALIVALLE766153360513647136983385.092B+32669.0PUBLICARNO
15429TIESTADOS UNIDOSM0002-06-30T00:00:00.00020201SB11202010026730ESTUDIANTEESTADOS UNIDOSNoVALLE76.0CALI76001.0Estrato 53 a 4DosEducación profesional completaPostgradoEs vendedor o trabaja en atención al públicoEs dueño de un negocio grande, tiene un cargo de nivel directivo o gerencialSiSiSiSiSiSiNoSi26 A 100 LIBROS3 a 5 veces por semanaTodos o casi todos los días3 a 5 veces por semanaMejorEntre 30 y 60 minutosMás de 3 horas0No17293376001003461COLEGIO SAN ANTONIO MARIA CLARETMIXTONO OFICIALBNACADÉMICO376001003461COLEGIO SAN ANTONIO MARIA CLARETSURBANOMAÑANA76001CALI76VALLEN76001CALIVALLE765636366673564835443274.063B129651.0PUBLICARNO
15430TIITALIAF0003-08-24T00:00:00.00020201SB11202010002849ESTUDIANTEITALIANoVALLE76.0CALI76001.0Estrato 41 a 2DosSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaPensionadoTrabaja por cuenta propia (por ejemplo plomero, electricista)SiSiSiSiSiSiNoSi26 A 100 LIBROS1 o 2 veces por semanaTodos o casi todos los díasTodos o casi todos los díasIgualEntre 30 y 60 minutosEntre 30 y 60 minutos0No17061376001001388COLEGIO LACORDAIREMASCULINONO OFICIALBNACADÉMICO376001001388COLEGIO LACORDAIRESURBANOCOMPLETA76001CALI76VALLEN76001CALIVALLE766461350252647236060373.062B130354.0PUBLICARNO
15431TIPAÍSES BAJOS - HOLANDAF2002-05-21T00:00:00.00020201SB11202010004430ESTUDIANTEPAÍSES BAJOS - HOLANDANoVALLE76.0CALI76001.0Estrato 53 a 4CuatroNo AplicaNo AplicaNo aplicaNo aplicaSiSiSiSiSiSiNoNoMÁS DE 100 LIBROSTodos o casi todos los díasTodos o casi todos los díasTodos o casi todos los díasIgualEntre 30 y 60 minutosEntre 30 y 60 minutos0No131250376001030302COLEGIO ANGLO AMERICANOMIXTONO OFICIALBNaNACADÉMICO376001030302COLEGIO ANGLO AMERICANOSURBANOCOMPLETA76001CALI76VALLEN76001CALIVALLE7670824728547291470863100.0100B+36691.0PUBLICARNO
15432TIVENEZUELAF2001-12-07T00:00:00.00020201SB11202010008503ESTUDIANTEVENEZUELANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 5NaNNaNPrimaria incompletaEducación profesional completaNaNNaNSiSiNaNNaNNaNNaNNaNNaN26 A 100 LIBROSTodos o casi todos los díasTodos o casi todos los días1 o 2 veces por semanaNaNMás de 2 horasMás de 3 horasNaNNaN53348311769003920COL BILING CLERMONTMIXTONO OFICIALBSACADÉMICO311769003920COL BILING CLERMONT - SEDE PRINCIPALSURBANOCOMPLETA11001BOGOTÁ D.C.11BOGOTÁN11001BOGOTÁ D.C.BOGOTÁ117185466683657536469379.077B+33776.0PUBLICARNO
15433TIVENEZUELAF2003-04-25T00:00:00.00020201SB11202010072919ESTUDIANTEVENEZUELANoNORTE SANTANDER54.0CÚCUTA54001.0Estrato 23 a 4UnoSecundaria (Bachillerato) incompletaEducación profesional completaTrabaja por cuenta propia (por ejemplo plomero, electricista)Trabaja como profesional (por ejemplo médico, abogado, ingeniero)NoNoNoSiNoNoSiNo0 A 10 LIBROS1 o 2 veces por semanaTodos o casi todos los días1 o 2 veces por semanaPeorEntre 1 y 2 horasNo Navega InternetMás de 30 horasSi, en efectivo175968354001012211I.E. PARA JÓVENES Y ADULTOS SIN FRONTERASMIXTONO OFICIALANACADÉMICO354001012211INSTITUCIÓN EDUCATIVA SINFRONTERAS - SEDE PRINCIPALSURBANOMAÑANA54001CÚCUTA54NORTE SANTANDERN54001CÚCUTANORTE SANTANDER545944348212523824730240.011A-25328.0PUBLICARGENERACION E - GRATUIDAD
15434TIVENEZUELAM0001-12-27T00:00:00.00020201SB11202010002521ESTUDIANTEVENEZUELANoVALLE76.0CALI76001.0Estrato 53 a 4TresSecundaria (Bachillerato) completaEducación profesional completaTrabaja por cuenta propia (por ejemplo plomero, electricista)Trabaja como profesional (por ejemplo médico, abogado, ingeniero)SiSiSiSiSiSiNoSi26 A 100 LIBROS1 o 2 veces por semanaTodos o casi todos los días3 a 5 veces por semanaIgualEntre 30 y 60 minutosMás de 3 horas0No17004376001002376COLEGIO HISPANOAMERICANOMIXTONO OFICIALBNACADÉMICO376001002376COLEGIO HISPANOAMERICANOSURBANOCOMPLETA76001CALI76VALLEN76001CALIVALLE767083472844759646881378.074B135988.0PUBLICARNO